U.S. patent application number 17/073156 was filed with the patent office on 2021-02-04 for methods for training a model for use in radio wave based blood pressure monitoring.
The applicant listed for this patent is MOVANO INC.. Invention is credited to Michael A. LEABMAN.
Application Number | 20210030282 17/073156 |
Document ID | / |
Family ID | 1000005150755 |
Filed Date | 2021-02-04 |
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United States Patent
Application |
20210030282 |
Kind Code |
A1 |
LEABMAN; Michael A. |
February 4, 2021 |
METHODS FOR TRAINING A MODEL FOR USE IN RADIO WAVE BASED BLOOD
PRESSURE MONITORING
Abstract
Methods for training a model for use in monitoring a health
parameter in a person are disclosed. In an embodiment, a method
involves monitoring a blood pressure of a person using a control
blood pressure monitoring system, receiving control data that
corresponds to the monitoring using the control blood pressure
monitoring system, receiving stepped frequency scanning data that
corresponds to radio waves that have reflected from blood in a
blood vessel of the person, wherein the stepped frequency scanning
data is collected through multiple receive antennas over a range of
frequencies, generating training data by combining the control data
with the stepped frequency scanning data in a time synchronous
manner, and training a model using the training data to produce a
trained model, wherein the trained model correlates stepped
frequency scanning data to values that are indicative of a blood
pressure of a person.
Inventors: |
LEABMAN; Michael A.; (San
Ramon, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MOVANO INC. |
San Ramon |
CA |
US |
|
|
Family ID: |
1000005150755 |
Appl. No.: |
17/073156 |
Filed: |
October 16, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16682928 |
Nov 13, 2019 |
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17073156 |
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62781523 |
Dec 18, 2018 |
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62894741 |
Aug 31, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/681 20130101;
A61B 5/02438 20130101; G01S 2007/028 20130101; A61B 5/489 20130101;
A61B 5/02141 20130101; A61B 5/742 20130101; A61B 5/02444 20130101;
A61B 5/021 20130101; G01S 13/88 20130101; A61B 5/14532 20130101;
A61B 5/0245 20130101; A61B 5/022 20130101; G01S 13/87 20130101;
H01Q 21/061 20130101; H01Q 1/273 20130101; A61B 5/725 20130101;
A61B 5/6898 20130101; A61B 5/7257 20130101; A61B 5/05 20130101;
G01S 7/025 20130101; G06F 1/163 20130101; G01S 13/53 20130101; G06N
20/00 20190101 |
International
Class: |
A61B 5/0245 20060101
A61B005/0245; A61B 5/05 20060101 A61B005/05; A61B 5/145 20060101
A61B005/145; H01Q 21/06 20060101 H01Q021/06; A61B 5/00 20060101
A61B005/00; H01Q 1/27 20060101 H01Q001/27; A61B 5/021 20060101
A61B005/021; A61B 5/024 20060101 A61B005/024; G01S 7/02 20060101
G01S007/02; G01S 13/88 20060101 G01S013/88; G06N 20/00 20060101
G06N020/00; A61B 5/022 20060101 A61B005/022; G01S 13/87 20060101
G01S013/87; G06F 1/16 20060101 G06F001/16; G01S 13/53 20060101
G01S013/53 |
Claims
1. A method for training a model for use in monitoring a health
parameter in a person, the method comprising: monitoring a blood
pressure of a person using a control blood pressure monitoring
system; receiving control data that corresponds to the monitoring
using the control blood pressure monitoring system; receiving
stepped frequency scanning data that corresponds to radio waves
that have reflected from blood in a blood vessel of the person,
wherein the stepped frequency scanning data is collected through
multiple receive antennas over a range of frequencies; generating
training data by combining the control data with the stepped
frequency scanning data in a time synchronous manner; and training
a model using the training data to produce a trained model, wherein
the trained model correlates stepped frequency scanning data to
values that are indicative of a blood pressure of a person.
2. The method of claim 1, wherein generating training data
comprises deriving data from the stepped frequency scanning
data.
3. The method of claim 2, wherein deriving data from the stepped
frequency scanning data comprises calculating a statistic from the
stepped frequency scanning data.
4. The method of claim 2, wherein deriving data from the stepped
frequency scanning data comprises calculating a standard deviation
from amplitude data of the stepped frequency scanning data.
5. The method of claim 2, wherein deriving data from the stepped
frequency scanning data comprises calculating a standard deviation
from phase data of the stepped frequency scanning data.
6. The method of claim 2, wherein training a model using the
training data comprises training a model using the derived
data.
7. The method of claim 2, wherein the derived data comprises a
statistic derived from the stepped frequency data and wherein
training a model using the training data comprises training a model
using the statistic.
8. The method of claim 1, wherein the stepped frequency scanning
data is generated by transmitting radio waves below the skin
surface of the person and receiving radio waves on a
two-dimensional array of receive antennas, the received radio waves
including a reflected portion of the transmitted radio waves that
is reflected from a blood vessel of the person.
9. The method of claim 8, wherein the stepped frequency scanning
data includes frequency and corresponding amplitude and phase data
over a range of frequencies for each of a plurality of receive
antennas in the two-dimensional array of receive antennas.
10. The method of claim 1, wherein the stepped frequency scanning
data includes frequency and corresponding amplitude and phase data
over a range of frequencies for each of a plurality of receive
antennas in a two-dimensional array of receive antennas.
11. The method of claim 1, wherein the stepped frequency scanning
data includes time, frequency, amplitude, phase, and antenna
orientation data over a range of frequencies for each of a
plurality of receive antennas in a two-dimensional array of receive
antennas.
12. The method of claim 1, wherein the control blood pressure
monitoring system is a clinically accepted blood pressure
monitoring system.
13. A method for training a model for use in monitoring a health
parameter in a person, the method comprising: monitoring a blood
pressure of a person using a control blood pressure monitoring
system; receiving control data that corresponds to the monitoring
using the control blood pressure monitoring system; transmitting
radio waves below the skin surface of the person and across a range
of stepped frequencies; receiving radio waves on a two-dimensional
array of receive antennas, the received radio waves including a
reflected portion of the transmitted radio waves across the range
of stepped frequencies that is reflected by blood in a blood vessel
below the skin surface of the person; generating stepped frequency
scanning data that corresponds to a change in reflectivity of the
blood in the blood vessel in response to the reflected portion of
the transmitted radio waves; generating training data by combining
the control data with the stepped frequency scanning data in a time
synchronous manner; and training a model using the training data to
produce a trained model, wherein the trained model correlates
stepped frequency scanning data to values that are indicative of a
blood pressure of a person.
14. The method of claim 13, wherein the generated data includes
amplitude and phase data, and wherein the phase data corresponds to
a phase shift in the received radio waves.
15. The method of claim 13, wherein radio waves are transmitted
from transmit antennas that have at least two different
polarization orientations and wherein radio waves are received on
antennas in the two-dimensional array of receive antennas that have
polarization orientations that correspond to the transmit
antennas.
16. The method of claim 13, wherein the control blood pressure
monitoring system is a clinically accepted blood pressure
monitoring system.
Description
BACKGROUND
[0001] Diabetes is a medical disorder in which a person's blood
glucose level, also known as blood sugar level, is elevated over an
extended period of time. If left untreated, diabetes can lead to
severe medical complications such as cardiovascular disease, kidney
disease, stroke, foot ulcers, and eye damage. It has been estimated
that the total cost of diabetes in the U.S. in 2017 was 327
billion, American Diabetes Association, "Economic Costs of Diabetes
in the U.S. in 2017," published online on Mar. 22, 2018.
[0002] Diabetes is typically caused by either the pancreas not
producing enough insulin, referred to as "Type 1" diabetes, or
because the cells of the person do not properly respond to insulin
that is produced, referred to as "Type 2" diabetes. Managing
diabetes may involve monitoring a person's blood glucose level and
administering insulin when the person's blood glucose level is too
high to bring the blood glucose level down to a desired level. A
person may need to measure their blood glucose level up to ten
times a day depending on many factors, including the severity of
the diabetes and the person's medical history. Billions of dollars
are spent each year on equipment and supplies used to monitor blood
glucose levels.
SUMMARY
[0003] Methods for training a model for use in monitoring a health
parameter in a person are disclosed. In one embodiment, a method
involves monitoring a blood pressure of a person using a control
blood pressure monitoring system, receiving control data that
corresponds to the monitoring using the control blood pressure
monitoring system, receiving stepped frequency scanning data that
corresponds to radio waves that have reflected from blood in a
blood vessel of the person, wherein the stepped frequency scanning
data is collected through multiple receive antennas over a range of
frequencies, generating training data by combining the control data
with the stepped frequency scanning data in a time synchronous
manner, and training a model using the training data to produce a
trained model, wherein the trained model correlates stepped
frequency scanning data to values that are indicative of a blood
pressure of a person.
[0004] In an embodiment, generating training data comprises
deriving data from the stepped frequency scanning data.
[0005] In an embodiment, deriving data from the stepped frequency
scanning data comprises calculating a statistic from the stepped
frequency scanning data.
[0006] In an embodiment, deriving data from the stepped frequency
scanning data involves calculating a standard deviation from
amplitude data of the stepped frequency scanning data.
[0007] In an embodiment, deriving data from the stepped frequency
scanning data involves calculating a standard deviation from phase
data of the stepped frequency scanning data.
[0008] In an embodiment, training a model using the training data
comprises training a model using the derived data.
[0009] In an embodiment, the derived data comprises a statistic
derived from the stepped frequency data and wherein training a
model using the training data comprises training a model using the
statistic.
[0010] In an embodiment, the stepped frequency scanning data is
generated by transmitting radio waves below the skin surface of the
person and receiving radio waves on a two-dimensional array of
receive antennas, the received radio waves including a reflected
portion of the transmitted radio waves that is reflected from a
blood vessel of the person. In a further embodiment, the stepped
frequency scanning data includes frequency and corresponding
amplitude and phase data over a range of frequencies for each of a
plurality of receive antennas in the two-dimensional array of
receive antennas.
[0011] In an embodiment, the stepped frequency scanning data
includes frequency and corresponding amplitude and phase data over
a range of frequencies for each of a plurality of receive antennas
in a two-dimensional array of receive antennas.
[0012] In an embodiment, the stepped frequency scanning data
includes time, frequency, amplitude, phase, and antenna orientation
data over a range of frequencies for each of a plurality of receive
antennas in a two-dimensional array of receive antennas.
[0013] In an embodiment, the control blood pressure monitoring
system is a clinically accepted blood pressure monitoring
system.
[0014] Another embodiment of a method for training a model for use
in monitoring a health parameter in a person is disclosed. The
method involves monitoring a blood pressure of a person using a
control blood pressure monitoring system, receiving control data
that corresponds to the monitoring using the control blood pressure
monitoring system, transmitting radio waves below the skin surface
of the person and across a range of stepped frequencies, receiving
radio waves on a two-dimensional array of receive antennas, the
received radio waves including a reflected portion of the
transmitted radio waves across the range of stepped frequencies
that is reflected by blood in a blood vessel below the skin surface
of the person, generating stepped frequency scanning data that
corresponds to a change in reflectivity of the blood in the blood
vessel in response to the reflected portion of the transmitted
radio waves, generating training data by combining the control data
with the stepped frequency scanning data in a time synchronous
manner, and training a model using the training data to produce a
trained model, wherein the trained model correlates stepped
frequency scanning data to values that are indicative of a blood
pressure of a person.
[0015] In an embodiment, the generated data includes amplitude and
phase data, and the phase data corresponds to a phase shift in the
received radio waves.
[0016] In an embodiment, radio waves are transmitted from transmit
antennas that have at least two different polarization orientations
and radio waves are received on antennas in the two-dimensional
array of receive antennas that have polarization orientations that
correspond to the transmit antennas.
[0017] In an embodiment, the control blood pressure monitoring
system is a clinically accepted blood pressure monitoring
system.
[0018] Other aspects in accordance with the invention will become
apparent from the following detailed description, taken in
conjunction with the accompanying drawings, illustrated by way of
example of the principles of the invention.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] FIGS. 1A and 1B are perspective views of a smartwatch.
[0020] FIG. 2A depicts a posterior view of a right hand with the
typical approximate location of the cephalic vein and the basilic
vein overlaid/superimposed.
[0021] FIG. 2B depicts the location of a cross-section of the wrist
from FIG. 2A.
[0022] FIG. 2C depicts the cross-section of the wrist from the
approximate location shown in FIG. 2B (as viewed in the direction
from the elbow to the hand).
[0023] FIG. 3 is a perspective view of human skin that includes a
skin surface, hairs, and the epidermis and dermis layers of the
skin.
[0024] FIG. 4A depicts a simplified version of the cross-section of
FIG. 2C, which shows the skin, the radius and ulna bones, and the
basilic vein.
[0025] FIG. 4B depicts the wrist cross-section of FIG. 4A in a case
where a smartwatch is attached to the wrist.
[0026] FIG. 4C illustrates, in two dimensions, an example of the
penetration depth (which corresponds to a 3D illumination space) of
radio waves transmitted from the sensor system of the smartwatch at
a frequency of 60 GHz and a transmission power of 15 dBm.
[0027] FIG. 4D illustrates, in two dimensions, an example of the
penetration depth (which corresponds to a 3D illumination space) of
radio waves transmitted from the sensor system of the smartwatch at
a frequency of 122-126 GHz and transmit power of 15 dBm.
[0028] FIG. 5 depicts a functional block diagram of an embodiment
of a sensor system that utilizes millimeter range radio waves to
monitor a health parameter such as the blood glucose level in a
person.
[0029] FIG. 6 depicts an expanded view of an embodiment of portions
of the sensor system of FIG. 5, including elements of the RF
front-end.
[0030] FIG. 7 depicts an embodiment of the IF/BB component shown in
FIG. 6.
[0031] FIG. 8A depicts an example embodiment of a plan view of an
IC device that includes two TX antennas and four antennas 846 as
well as some of the components from the RF front-end and the
digital baseband (not shown) as described above with regard to
FIGS. 5-7.
[0032] FIG. 8B depicts an embodiment of a microstrip patch antenna
that can be used for the TX and/or RX antennas of the IC device of
FIG. 8A.
[0033] FIG. 8C depicts an example of the physical layout of circuit
components on a semiconductor substrate, such as the semiconductor
substrate (die) depicted in FIG. 8A.
[0034] FIG. 8D depicts a packaged IC device similar to the packaged
IC device shown in FIG. 8A superimposed over the semiconductor
substrate shown in FIG. 8C.
[0035] FIG. 9 depicts an IC device similar to that of FIG. 8A
overlaid on the hand/wrist that is described above with reference
to FIG. 2A-2C.
[0036] FIG. 10 depicts an IC device similar to that of FIG. 8A
overlaid on the back of the smartwatch.
[0037] FIG. 11 depicts a side view of a sensor system in a case in
which the two TX antennas are configured parallel to veins such as
the basilic and cephalic veins of a person wearing the
smartwatch.
[0038] FIG. 12 depicts the same side view as shown in FIG. 11 in a
case in which the two TX antennas are configured transverse to
veins such as the basilic and cephalic veins of a person wearing
the smartwatch.
[0039] FIGS. 13A-13C depict frequency versus time graphs of
impulse, chirp, and stepped frequency techniques for transmitting
electromagnetic energy in a radar system.
[0040] FIG. 14 depicts a burst of electromagnetic energy using
stepped frequency transmission.
[0041] FIG. 15A depicts a graph of the transmission bandwidth, B,
of transmitted electromagnetic energy in the frequency range of
122-126 GHz.
[0042] FIG. 15B depicts a graph of stepped frequency pulses that
have a repetition interval, T, and a step size, .DELTA.f, of 62.5
MHz.
[0043] FIG. 16A depicts a frequency versus time graph of
transmission pulses, with transmit (TX) interval and receive (RX)
intervals identified relative to the pulses.
[0044] FIG. 16B depicts an amplitude versus time graph of the
transmission waveforms that corresponds to FIG. 16A.
[0045] FIG. 17 illustrates operations related to transmitting,
receiving, and processing phases of the sensor system
operation.
[0046] FIG. 18 depicts an expanded view of the anatomy of a wrist,
similar to that described above with reference to FIGS. 2A-4D,
relative to RX antennas of a sensor system that is integrated into
a wearable device such as a smartwatch.
[0047] FIG. 19 illustrates an IC device similar to the IC device
shown in FIG. 8A relative to a vein and blood flowing through the
vein.
[0048] FIG. 20 is an embodiment of a DSP that includes a Doppler
effect component, a beamforming component, and a ranging
component.
[0049] FIG. 21 is a process flow diagram of a method for monitoring
a health parameter in a person.
[0050] FIG. 22A depicts a side view of the area around a person's
ear with the typical approximate locations of veins and arteries,
including the superficial temporal artery, the superficial temporal
vein, the anterior auricular artery and vein, the posterior
auricular artery, the occipital artery, the external carotid
artery, and the external jugular vein.
[0051] FIG. 22B depicts an embodiment of system in which at least
elements of an RF front-end are located separate from a
housing.
[0052] FIG. 22C illustrates how a device, such as the device
depicted in FIG. 22B, may be worn near the ear of a person similar
to how a conventional hearing aid is worn.
[0053] FIG. 23 is a table of parameters related to stepped
frequency scanning in a system such as the above-described
system.
[0054] FIG. 24 is a table of parameters similar to the table of
FIG. 23 in which examples are associated with each parameter for a
given step in a stepped frequency scanning operation in order to
give some context to the table.
[0055] FIG. 25 depicts an embodiment of the IC device from FIG. 8A
in which the antenna polarization orientation is illustrated by the
orientation of the transmit and receive antennas.
[0056] FIG. 26 is a table of raw data that is generated during
stepped frequency scanning
[0057] FIG. 27 illustrates a system and process for machine
learning that can be used to identify and train a model that
reflects correlations between raw data, derived data, and control
data.
[0058] FIG. 28 is an example of a process flow diagram of a method
for implementing machine learning.
[0059] FIG. 29 is an example of a table of a raw data record
generated during stepped frequency scanning that is used to
generate the training data.
[0060] FIGS. 30A-30D are tables of at least portions of raw data
records that are generated during a learning process that spans the
time of t1-tn, where n corresponds to the number of time intervals,
T, in the stepped frequency scanning
[0061] FIG. 31 illustrates a system for health parameter monitoring
that utilizes a sensor system similar to or the same as the sensor
system described with reference to FIGS. 5-7.
[0062] FIG. 32 is a process flow diagram of a method for monitoring
a health parameter in a person.
[0063] FIG. 33 is a process flow diagram of another method for
monitoring a health parameter in a person.
[0064] FIG. 34 is a process flow diagram of a method for training a
model for use in monitoring a health parameter in a person.
[0065] Throughout the description, similar reference numbers may be
used to identify similar elements.
DETAILED DESCRIPTION
[0066] It will be readily understood that the components of the
embodiments as generally described herein and illustrated in the
appended figures could be arranged and designed in a wide variety
of different configurations. Thus, the following more detailed
description of various embodiments, as represented in the figures,
is not intended to limit the scope of the present disclosure, but
is merely representative of various embodiments. While the various
aspects of the embodiments are presented in drawings, the drawings
are not necessarily drawn to scale unless specifically
indicated.
[0067] The present invention may be embodied in other specific
forms without departing from its spirit or essential
characteristics. The described embodiments are to be considered in
all respects only as illustrative and not restrictive. The scope of
the invention is, therefore, indicated by the appended claims
rather than by this detailed description. All changes which come
within the meaning and range of equivalency of the claims are to be
embraced within their scope.
[0068] Reference throughout this specification to features,
advantages, or similar language does not imply that all of the
features and advantages that may be realized with the present
invention should be or are in any single embodiment of the
invention. Rather, language referring to the features and
advantages is understood to mean that a specific feature,
advantage, or characteristic described in connection with an
embodiment is included in at least one embodiment of the present
invention. Thus, discussions of the features and advantages, and
similar language, throughout this specification may, but do not
necessarily, refer to the same embodiment.
[0069] Furthermore, the described features, advantages, and
characteristics of the invention may be combined in any suitable
manner in one or more embodiments. One skilled in the relevant art
will recognize, in light of the description herein, that the
invention can be practiced without one or more of the specific
features or advantages of a particular embodiment. In other
instances, additional features and advantages may be recognized in
certain embodiments that may not be present in all embodiments of
the invention.
[0070] Reference throughout this specification to "one embodiment",
"an embodiment", or similar language means that a particular
feature, structure, or characteristic described in connection with
the indicated embodiment is included in at least one embodiment of
the present invention. Thus, the phrases "in one embodiment", "in
an embodiment", and similar language throughout this specification
may, but do not necessarily, all refer to the same embodiment.
[0071] Traditional blood glucose level monitoring is accomplished
by pricking a finger to draw blood and measuring the blood glucose
level with a blood glucose meter, or "glucometer." Continuous
glucose monitoring can be accomplished by applying a continuous
glucose monitor (CGM) to an area on the body such as the torso. The
continuous glucose monitor utilizes a needle that is continuously
embedded through the skin to obtain access to blood. Although blood
glucose meters and continuous glucose monitors work well to monitor
blood glucose levels, both techniques are invasive in nature in
that they require physical penetration of the skin by a sharp
object.
[0072] Various non-invasive techniques for monitoring blood glucose
levels have been explored. Example techniques for monitoring blood
glucose levels include techniques based on infrared (IR)
spectroscopy, near infrared (NIR) spectroscopy, mid infrared (MIR)
spectroscopy, photoacoustic spectroscopy, fluorescence
spectroscopy, Raman spectroscopy, optical coherence tomography
(OCT), and microwave sensing, Ruochong Zhang et al., "Noninvasive
Electromagnetic Wave Sensing of Glucose," Oct. 1, 2018.
[0073] In the category of microwave sensing, millimeter range radio
waves have been identified as useful for monitoring blood glucose
levels. An example of using millimeter range radio waves to monitor
blood glucose levels is described by Peter H. Siegel et al.,
"Millimeter-Wave Non-Invasive Monitoring of Glucose in Anesthetized
Rats," 2014 International Conference on Infrared, Millimeter, and
Terahertz Waves, Tucson, Ariz., Sep. 14-19, 2014. Here, Siegel et
al. describes using the Ka band (27-40 GHz) to measure blood
glucose levels through the ear of a lab rat.
[0074] Another example of using millimeter range radio waves to
monitor blood glucose levels is described by George Shaker et al.,
"Non-Invasive Monitoring of Glucose Level Changes Utilizing a
mm-Wave Radar System," International Journal of Mobile Human
Computer Interaction, Volume 10, Issue 3, July-September 2018.
Here, Shaker et al. utilizes a millimeter range sensing system
referred to as "Soli," (see Jaime Lien et. al., "Soli: Ubiquitous
Gesture Sensing with Millimeter Wave Radar," ACM Trans. Graph. 35,
4 Article 142, July 2016) to monitor blood glucose levels. Shaker
et al. utilizes radio waves in the 57-64 GHz frequency range to
monitor blood glucose levels. Although the Soli sensor system
includes transmit (TX) and receive (RX) antennas on the same
integrated circuit (IC) device (i.e., the same "chip") and thus in
the same plane, Shaker et al. concludes that for blood glucose
monitoring, a radar sensing system configuration would ideally have
its antennas placed on opposite sides of the sample under test to
be able to effectively monitor blood glucose levels. When the
transmit (TX) and receive (RX) antennas were on the same side of
the sample under test, Shaker et al. was not able to find any
discernible trend in the magnitude or phase of the sensor
signals.
[0075] Another example of using millimeter range radio waves to
monitor blood glucose levels is described by Shimul Saha et al., "A
Glucose Sensing System Based on Transmission Measurements at
Millimeter Waves using Micro strip Patch Antennas," Scientific
Reports, published online Jul. 31, 2017. Here, Saha et al. notes
that millimeter wave spectroscopy in reflection mode has been used
for non-invasive glucose sensing through human skin, but concludes
that signals from reflection mode detection yield information that
is insufficient for tracking the relevant changes in blood glucose
levels. Saha et al. investigates radio waves in the range of 20-100
GHz for monitoring blood glucose levels and concludes that an
optimal sensing frequency is in the range of 40-80 GHz.
[0076] Although blood glucose level monitoring using millimeter
range radio waves has been shown to be technically feasible,
implementation of practical monitoring methods and systems has yet
to be realized. For example, a practical realization of a
monitoring system may include a monitoring system that can be
integrated into a wearable device, such as a smartwatch.
[0077] In accordance with an embodiment of the invention, methods
and systems for monitoring the blood glucose level of a person
using millimeter range radio waves involve transmitting millimeter
range radio waves below the skin surface, receiving a reflected
portion of the radio waves on multiple receive antennas, isolating
a signal from a particular location in response to the received
radio waves, and outputting a signal that corresponds to a blood
glucose level in the person in response to the isolated signals. In
an embodiment, beamforming is used in the receive process to
isolate radio waves that are reflected from a specific location
(e.g., onto a specific blood vessel) to provide a high-quality
signal that corresponds to blood glucose levels in the specific
blood vessel. In another embodiment, Doppler effect processing can
be used to isolate radio waves that are reflected from a specific
location (e.g., reflected from a specific blood vessel) to provide
a high-quality signal that corresponds to blood glucose levels in
the specific blood vessel. Analog and/or digital signal processing
techniques can be used to implement beamforming and/or Doppler
effect processing and digital signal processing of the received
signals can be used to dynamically adjust (or "focus") a received
beam onto the desired location. In still another embodiment,
beamforming and Doppler effect processing can be used together to
isolate radio waves that are reflected from a specific location
(e.g., reflected from a specific blood vessel) to provide a
high-quality signal that corresponds to blood glucose levels in the
specific blood vessel.
[0078] As described above, Siegal et al., Shaker et al., and Saha
et al., utilize radio waves in the range of about 27-80 GHz,
commonly around 60 GHz, to monitor blood glucose levels. Saha et
al. discloses that a frequency of around 60 GHz is desirable for
glucose detection using electromagnetic transmission data and notes
that for increasingly higher frequencies, the losses are
prohibitively high for the signal-to-noise ratio (SNR) to exceed
the noise level of a sensing instrument such as a Vector Network
Analyzer (VNA).
[0079] In contrast to conventional techniques, it has been
discovered that using a higher frequency range, e.g., 122-126 GHz,
to monitor blood glucose levels can provide certain benefits that
heretofore have not been recognized. For example, transmitting
millimeter range radio waves in the frequency range of 122-126 GHz
results in a shallower penetration depth within a human body than
radio waves in the frequency range around 60 GHz for a similar
transmission power. A shallower penetration depth can reduce
undesirable reflections (e.g., reflections off of bone and dense
tissue such as tendons, ligaments, and muscle), which can reduce
the signal processing burden and improve the quality of the desired
signal that is generated from the location of a blood vessel.
[0080] Additionally, transmitting millimeter range radio waves in
the frequency range of 122-126 GHz enables higher resolution
sensing than radio waves at around 60 GHz due to the shorter
wavelengths, e.g., 2.46-2.38 mm for 122-126 GHz radio waves versus
5 mm for 60 GHz radio waves. Higher resolution sensing allows a
receive beam to be focused more precisely (e.g., through
beamforming and/or Doppler effect processing) onto a particular
blood vessel, such as the basilic vein on the posterior of the
wrist, which can also improve the quality of the desired
signal.
[0081] Additionally, utilizing millimeter range radio waves in the
frequency range of 122-126 GHz to monitor blood glucose levels
enables the size of the corresponding transmit and receive antennas
to be reduced in comparison to techniques that utilize radio waves
in the frequency range of 20-80 GHz. For example, the size of
antennas can be reduced by a factor of approximately two by using
radio waves in the 122-126 GHz frequency range instead of radio
waves in the 60 GHz frequency range, which can enable a smaller
form factor for the antennas and for the overall sensor system.
Additionally, the frequency range of 122-126 GHz is an unlicensed
band of the industrial, scientific, and medical (ISM) radio bands
as defined by the International Telecommunication Union (ITU) Radio
Regulations. Thus, methods and systems for monitoring blood glucose
levels that are implemented using a frequency range of 122-126 GHz
do not require a license.
[0082] FIGS. 1A and 1B are perspective views of a smartwatch 100,
which is a device that provides various computing functionality
beyond simply giving the time. Smartwatches are well known in the
field. The smartwatch includes a case 102 (also referred to as a
"housing") and a strap 104 (e.g., an attachment device) and the
strap is typically attached to the case by lugs (not shown). FIG.
1A is a top perspective view of the smartwatch that shows a front
face 106 of the case and a crown 108 and FIG. 1B is a back
perspective view of the smartwatch that shows a back plate of the
case. FIG. 1B also includes a dashed line block 110 that represents
a sensor system, such as a sensor system for health monitoring. The
sensor system may be partially or fully embedded within the case.
In some embodiments, the sensor system may include a sensor
integrated circuit (IC) device or IC devices with transmit and/or
receive antennas integrated therewith. In some embodiments, the
back plate of the case may have openings that allow radio waves to
pass more easily to and from smartwatch. In some embodiments, the
back plate of the case may have areas of differing materials that
create channels through which radio waves can pass more easily. For
example, in an embodiment, the back plate of the case may be made
primarily of metal with openings in the metal at locations that
correspond to sensor antennas that are filled with a material
(e.g., plastic or glass) that allows radio waves to pass to and
from the smartwatch more easily than through the metal case.
[0083] Although a smartwatch is described as one device in which a
millimeter range radio wave sensing system can be included, a
millimeter range radio wave sensing system can be included in other
sensing devices, including various types of wearable devices and/or
devices that are not wearable but that are brought close to, or in
contact with, the skin of a person only when health monitoring is
desired. For example, a millimeter range radio wave sensing system
can be incorporated into a smartphone. In an embodiment, a
millimeter range radio wave sensing system can be included in a
health and fitness tracking device that is worn on the wrist and
tracks, among other things, a person's movements. In another
embodiment, a millimeter range radio wave sensing system can be
incorporated into a device such as dongle or cover (e.g., a
protective cover that is placed over a smartphone for protection)
that is paired (e.g., via a local data connection such as USB or
BLUETOOTH) with a device such as a smartphone or smartwatch to
implement health monitoring. For example, a dongle may include many
of the components described below with reference to FIG. 6, while
the paired device (e.g., the smartphone or smartwatch) includes a
digital signal processing capability (e.g., through a Digital
Signal Processor (DSP)) and instruction processing capability
(e.g., through a Central Processing Unit (CPU)). In another
example, a millimeter range sensing system may be incorporated into
a device that is attached to the ear. In an embodiment, the sensing
system could be attached to the lobe of the ear or have an
attachment element that wraps around the ear or wraps around a
portion of the ear.
[0084] Wearable devices such as smartwatches and health and fitness
trackers are often worn on the wrist similar to a traditional
wristwatch. In order to monitor blood glucose levels using
millimeter range radio waves, it has been discovered that the
anatomy of the wrist is an important consideration. FIG. 2A depicts
a posterior view of a right hand 212 with the typical approximate
location of the cephalic vein 214 and the basilic vein 216
overlaid/superimposed. FIG. 2B depicts the location of a
cross-section of the wrist 218 from FIG. 2A and FIG. 2C depicts the
cross-section of the wrist 218 from the approximate location shown
in FIG. 2B (as viewed in the direction from the elbow to the hand).
In FIG. 2C, the cross-section is oriented on the page such that the
posterior portion of the wrist is on the top and the anterior
portion of the wrist is on the bottom. The depth dimension of a
wrist is identified on the left side and typically ranges from
40-60 mm (based on a wrist circumference in the range of 140-190
mm). Anatomic features of the wrist shown in FIG. 2C include the
abductor pollicis longus (APL), the extensor carpi radialis brevis
(ECRB), the extensor carpi radialis longus (ECRL), the extensor
carpi ulnaris (ECU), the extensor indicis proprius (EIP), the
extensor pollicis brevis (EPB), the extensor pollicis longus (EPL),
the flexor carpi ulnaris (FCU), the flexor digitorum superficialis
(FDS), the flexor pollicis longus (FPL), the basilic vein 216, the
radius, the ulna, the radial artery, the median nerve, the ulnar
artery, and the ulnar nerve. FIG. 2C also depicts the approximate
location of the basilic vein in subcutaneous tissue 220 below the
skin 222. In some embodiments and as is disclosed below, the
location of a blood vessel such as the basilic vein is of
particular interest to monitoring blood glucose levels using
millimeter range radio waves.
[0085] FIG. 3 is a perspective view of human skin 322 that includes
a skin surface 324, hairs 326, and the epidermis 328 and dermis 330
layers of the skin. The skin is located on top of subcutaneous
tissue 320. In an example, the thickness of human skin in the wrist
area is around 1-4 mm and the thickness of the subcutaneous tissue
may vary from 1-34 mm, although these thicknesses may vary based on
many factors. As shown in FIG. 3, very small blood vessels 332
(e.g., capillaries having a diameter in the range of approximately
5-10 microns) are located around the interface between the dermis
and the subcutaneous tissue while veins, such as the cephalic and
basilic veins, are located in the subcutaneous tissue just below
the skin. For example, the cephalic and basilic veins may have a
diameter in the range of 1-4 mm and may be approximately 2-10 mm
below the surface of the skin, although these diameters and depths
may vary based on many factors. FIG. 3 depicts an example location
of the basilic vein 316 in the area of the wrist.
[0086] FIG. 4A depicts a simplified version of the cross-section of
FIG. 2C, which shows the skin 422, the radius and ulna bones 434
and 436, and the basilic vein 416. FIG. 4B depicts the wrist
cross-section of FIG. 4A in a case where a smartwatch 400, such as
the smartwatch shown in FIGS. 1A and 1B, is attached to the wrist.
FIG. 4B illustrates an example of the location of the smartwatch
relative to the wrist and in particular relative to the basilic
vein of the wrist. In the example of FIG. 4B, dashed line block 410
represents the approximate location of a sensor system and
corresponds to the dashed line block 110 shown in FIG. 1B. The
location of the smartwatch relative to the anatomy of the wrist,
including the bones and a vein such as the basilic vein, is an
important consideration in implementing blood glucose monitoring
using millimeter range radio waves.
[0087] The magnitude of the reflected and received radio waves is a
function of the power of the transmitted radio waves. With regard
to the anatomy of the human body, it has been realized that radio
waves transmitted at around 60 GHz at a particular transmission
power level (e.g., 15 dBm) penetrate deeper (and thus illuminate a
larger 3D space) into the human body than radio waves transmitted
at 122-126 GHz at the same transmission power level (e.g., 15 dBm).
FIG. 4C illustrates, in two dimensions, an example of the
penetration depth (which corresponds to a 3D illumination space) of
radio waves 438 transmitted from the sensor system of the
smartwatch at a frequency of 60 GHz and a transmission power of 15
dBm. FIG. 4D illustrates, in two dimensions, an example of the
penetration depth (which corresponds to a 3D illumination space) of
radio waves 440 transmitted from the sensor system of the
smartwatch at a frequency of 122-126 GHz and transmit power of 15
dBm, which is the same transmission power as used in the example of
FIG. 4C. As illustrated by FIGS. 4C and 4D, for equivalent
transmission powers (e.g., 15 dBm), radio waves 438 transmitted at
60 GHz penetrate deeper into the wrist (and thus have a
corresponding larger illumination space) than radio waves 440 that
are transmitted at 122-126 GHz. The deeper penetration depth of the
60 GHz radio waves results in more radio waves being reflected from
anatomical features within the wrist. For example, a large quantity
of radio waves will be reflected from the radius and ulna bones 434
and 436 in the wrist as well as from dense tissue such as tendons
and ligaments that are located between the skin and the bones at
the posterior of the wrist, see FIG. 2C, which shows tendons and
ligaments that are located between the skin and the bones at the
posterior of the wrist. Likewise the shallower penetration of the
122-126 GHz radio waves results in fewer radio waves being
reflected from undesired anatomical features within the wrist
(e.g., anatomical features other than the targeted blood vessel or
vein). For example, a much smaller or negligible magnitude of radio
waves will be reflected from the radius and ulna bones in the wrist
as well as from dense tissue such as tendons and ligaments that are
located between the skin and the bones at the posterior of the
wrist.
[0088] It has been realized that the penetration depth (and
corresponding 3D illumination space), is an important factor in the
complexity of the signal processing that is performed to obtain an
identifiable signal that corresponds to the blood glucose level in
the wrist (e.g., in the basilic vein of the wrist). In order to
accurately measure the blood glucose level in a vein such as the
basilic vein, it is desirable to isolate reflections from the area
of the vein from all of the other reflections that are detected
(e.g., from reflections from the radius and ulna bones in the wrist
as well as from dense tissue such as tendons and ligaments that are
located between the skin and the bones at the posterior of the
wrist). In an embodiment, radio waves are transmitted at an initial
power such that the power of the radio waves has diminished by
approximately one-half (e.g., .+-.10%) at a depth of 6 mm below the
skin surface. Reflections can be isolated using various techniques
including signal processing techniques that are used for
beamforming, Doppler effect, and/or leakage mitigation. The larger
quantity of reflections in the 60 GHz case will likely need more
intensive signal processing to remove signals that correspond to
unwanted reflections in order to obtain a signal of sufficient
quality to monitor a blood parameter such as the blood glucose
level in a person.
[0089] FIG. 5 depicts a functional block diagram of an embodiment
of a sensor system 510 that utilizes millimeter range radio waves
to monitor a health parameter such as the blood glucose level in a
person. The sensor system includes transmit (TX) antennas 544,
receive (RX) antennas 546, an RF front-end 548, a digital baseband
system 550, and a CPU 552. The components of the sensor system may
be integrated together in various ways. For example, some
combination of components may be fabricated on the same
semiconductor substrate and/or included in the same packaged IC
device or a combination of packaged IC devices. As described above,
in an embodiment, the sensor system is designed to transmit and
receive radio waves in the range of 122-126 GHz.
[0090] In the embodiment of FIG. 5, the sensor system 510 includes
two TX antennas 544 and four RX antennas 546. Although two TX and
four RX antennas are used, there could be another number of
antennas, e.g., one or more TX antennas and two or more RX
antennas. In an embodiment, the antennas are configured to transmit
and receive millimeter range radio waves. For example, the antennas
are configured to transmit and receive radio waves in the 122-126
GHz frequency range, e.g., wavelengths in the range of 2.46-2.38
mm
[0091] In the embodiment of FIG. 5, the RF front-end 548 includes a
transmit (TX) component 554, a receive (RX) component 556, a
frequency synthesizer 558, and an analogue processing component
560. The transmit component may include elements such as power
amplifiers and mixers. The receive component may include elements
such as low noise amplifiers (LNAs), variable gain amplifiers
(VGAs), and mixers. The frequency synthesizer includes elements to
generate electrical signals at frequencies that are used by the
transmit and receive components. In an embodiment the frequency
synthesizer may include elements such as a crystal oscillator, a
phase-locked loop (PLL), a frequency doubler, and/or a combination
thereof. The analogue processing component may include elements
such as mixers and filters, e.g., low pass filters (LPFs). In an
embodiment, components of the RF front-end are implemented in
hardware as electronic circuits that are fabricated on the same
semiconductor substrate.
[0092] The digital baseband system 550 includes an
analog-to-digital converter (ADC) 562, a digital signal processor
(DSP) 564, and a microcontroller unit (MCU) 566. Although the
digital baseband system is shown as including certain elements, the
digital baseband system may include some other configuration,
including some other combination of elements. The digital baseband
system is connected to the CPU 552 via a bus.
[0093] FIG. 6 depicts an expanded view of an embodiment of portions
of the sensor system 510 of FIG. 5, including elements of the RF
front-end. In the embodiment of FIG. 6, the elements include a
crystal oscillator 670, a phase locked loop (PLL) 672, a bandpass
filter (BPF) 674, a mixer 676, power amplifiers (PAs) 678, TX
antennas 644, a frequency synthesizer 680, a frequency doubler 682,
a frequency divider 684, a mixer 686, an RX antenna 646, a low
noise amplifier (LNA) 688, a mixer 690, a mixer 692, and an
Intermediate Frequency/Baseband (IF/BB) component 694. As
illustrated in FIG. 6, the group of receive components identified
within and dashed box 696 is repeated four times, e.g., once for
each of four distinct RX antennas.
[0094] Operation of the system shown in FIG. 6 is described with
reference to a transmit operation and with reference to a receive
operation. The description of a transmit operation generally
corresponds to a left-to-right progression in FIG. 6 and
description of a receive operation generally corresponds to a
right-to-left progression in FIG. 6. With regard to the transmit
operation, the crystal oscillator 670 generates an analog signal at
a frequency of 10 MHz. The 10 MHz signal is provided to the PLL
672, to the frequency synthesizer 680, and to the frequency divider
684. The PLL uses the 10 MHz signal to generate an analog signal
that is in the 2-6 GHz frequency range. The 2-6 GHz signal is
provided to the BPF 674, which filters the input signal and passes
a signal in the 2-6 GHz range to the mixer 676. The 2-6 GHz signal
is also provided to the mixer 686.
[0095] Dropping down in FIG. 6, the 10 MHz signal is used by the
frequency synthesizer 680 to produce a 15 GHz signal. The 15 GHz
signal is used by the frequency doubler 682 to generate a signal at
120 GHz. In an embodiment, the frequency doubler includes a series
of three frequency doublers that each double the frequency, e.g.,
from 15 GHz to 30 GHz, and then from 30 GHz to 60 GHz, and then
from 60 GHz to 120 GHz. The 120 GHz signal and the 2-6 GHz signal
are provided to the mixer 676, which mixes the two signals to
generate a signal at 122-126 GHz depending on the frequency of the
2-6 GHz signal. The 122-126 GHz signal output from the mixer 676 is
provided to the power amplifiers 678, and RF signals in the 122-126
GHz range are output from the TX antennas 644. In an embodiment,
the 122-126 GHz signals are output at 15 dBm (decibels (dB) with
reference to 1 milliwatt (mW)). In an embodiment and as described
below, the PLL is controlled to generate discrete frequency pulses
between 2-6 GHz that are used for stepped frequency
transmission.
[0096] The 10 MHz signal from the crystal oscillator 670 is also
provided to the frequency divider 684, which divides the frequency
down, e.g., from 10 MHz to 2.5 MHz via, for example, two divide by
two operations, and provides an output signal at 2.5 MHz to the
mixer 686. The mixer 686 also receives the 2-6 GHz signal from the
BPF 674 and provides a signal at 2-6 GHz+2.5 MHz to the mixer 692
for receive signal processing.
[0097] With reference to a receive operation, electromagnetic (EM)
energy is received at the RX antenna 646 and converted to
electrical signals, e.g., voltage and current. For example,
electromagnetic energy in the 122-126 GHz frequency band is
converted to an electrical signal that corresponds in frequency
(e.g., GHz), magnitude (e.g., power in dBm), and phase to the
electromagnetic energy that is received at the RX antenna. The
electrical signal is provided to the LNA 688. In an embodiment, the
LNA amplifies signals in the 122-126 GHz frequency range and
outputs an amplified 122-126 GHz signal. The amplified 122-126 GHz
signal is provided to the mixer 690, which mixes the 120 GHz signal
from the frequency doubler 682 with the received 122-126 GHz signal
to generate a 2-6 GHz signal that corresponds to the
electromagnetic energy that was received at the RX antenna. The 2-6
GHz signal is then mixed with the 2-6 GHz+2.5 MHz signal at mixer
692 to generate a 2.5 MHz signal that corresponds to the
electromagnetic energy that was received at the RX antenna. For
example, when a 122 GHz signal is being transmitted from the TX
antennas and received at the RX antenna, the mixer 692 receives a 2
GHz signal that corresponds to the electromagnetic energy that was
received at the antenna and a 2 GHz+2.5 MHz signal from the mixer
686. The mixer 692 mixes the 2 GHz signal that corresponds to the
electromagnetic energy that was received at the RX antenna with the
2 GHz+2.5 MHz signal from the mixer 686 to generate a 2.5 MHz
signal that corresponds to the electromagnetic energy that was
received at the RX antenna. The 2.5 MHz signal that corresponds to
the electromagnetic energy that was received at the RX antenna is
provided to the IF/BB component 694 for analog-to-digital
conversion. The above-described receive process can be implemented
in parallel on each of the four receive paths 696. As is described
below, the system described with reference to FIG. 6 can be used to
generate various discrete frequencies that can be used to
implement, for example, stepped frequency radar detection. As
described above, multiple mixing operations are performed to
implement a sensor system at such a high frequency, e.g., in the
122-126 GHz range. The multiple mixers and corresponding mixing
operations implement a "compound mixing" architecture that enables
use of such high frequencies.
[0098] FIG. 7 depicts an embodiment of the IF/BB component 794
shown in FIG. 6. The IF/BB component of FIG. 7 includes similar
signal paths 702 for each of the four receive paths/RX antennas and
each signal path includes a low pass filter (LPF) 704, an
analog-to-digital converter (ADC) 762, a mixer 706, and a
decimation filter 708. The operation of receive path 1, RX1, is
described.
[0099] As described above with reference to FIG. 6, the 2.5 MHz
signal from mixer 692 (FIG. 6) is provided to the IF/BB component
694/794, in particular, to the LPF 704 of the IF/BB component 794.
In an embodiment, the LPF filters the 2.5 MHz signal to remove the
negative frequency spectrum and noise outside of the desired
bandwidth. After passing through the LPF, the 2.5 MHz signal is
provided to the ADC 762, which converts the 2.5 MHz signal (e.g.,
IF signal) to digital data at a sampling rate of 10 MHz (e.g., as
12-16 bits of "real" data). The mixer 706 multiplies the digital
data with a complex vector to generate a digital signal (e.g.,
12-16 bits of "complex" data), which is also sampled at 10 MHz.
Although the signal is sampled at 10 MHz, other sampling rates are
possible, e.g., 20 MHz. The digital data sampled at 10 MHz is
provided to the decimation filter, which is used to reduce the
amount of data by selectively discarding a portion of the sampled
data. For example, the decimation filter reduces the amount of data
by reducing the sampling rate and getting rid of a certain
percentage of the samples, such that fewer samples are retained.
The reduction in sample retention can be represented by a
decimation factor, M, and may be, for example, about 10 or 100
depending on the application, where M equals the input sample rate
divided by the output sample rate.
[0100] The output of the decimation filter 706 is digital data that
is representative of the electromagnetic energy that was received
at the corresponding RX antenna. In an embodiment, samples are
output from the IF/BB component 794 at rate of 1 MHz (using a
decimation factor of 10) or at a rate of 100 kHz (using a
decimation factor of 100). The digital data is provided to a DSP
and/or CPU 764 via a bus 710 for further processing. For example,
the digital data is processed to isolate a signal from a particular
location, e.g., to isolate signals that correspond to
electromagnetic energy that was reflected by the blood in a vein of
the person. In an embodiment, signal processing techniques are
applied to implement beamforming, Doppler effect processing, and/or
leakage mitigation to isolate a desired signal from other undesired
signals.
[0101] In conventional RF systems, the analog-to-digital conversion
process involves a high direct current (DC), such that the I
("real") and Q ("complex") components of the RF signal at DC are
lost at the ADC. Using the system as described above with reference
to FIGS. 5-7, the intermediate IF is not baseband, so I and Q can
be obtained after analog-to-digital conversion and digital mixing
as shown in FIG. 7.
[0102] In an embodiment, digital signal processing of the received
signals may involve implementing Kalman filters to smooth out noisy
data. In another embodiment, digital signal processing of the
received signals may involve combining receive chains digitally.
Other digital signal processing may be used to implement
beamforming, Doppler effect processing, and ranging. Digital signal
processing may be implemented in a DSP and/or in a CPU.
[0103] In an embodiment, certain components of the sensor system
are integrated onto a single semiconductor substrate and/or onto a
single packaged IC device (e.g., a packaged IC device that includes
multiple different semiconductor substrates (e.g., different die)
and antennas). For example, elements such as the components of the
RF front-end 548, and/or components of the digital baseband system
550 (FIGS. 5-7) are integrated onto the same semiconductor
substrate (e.g., the same die). In an embodiment, components of the
sensor system are integrated onto a single semiconductor substrate
that is approximately 5 mm.times.5 mm. In an embodiment, the TX
antennas and RX antennas are attached to an outer surface of the
semiconductor substrate and/or to an outer surface of an IC package
and electrically connected to the circuits integrated into the
semiconductor substrate. In an embodiment, the TX and RX antennas
are attached to the outer surface of the IC package such that the
TX and RX antenna attachments points are very close to the
corresponding transmit and receive circuits such as the PAs and
LNAs. In an embodiment, the semiconductor substrate and the
packaged IC device includes outputs for outputting electrical
signals to another components such as a DSP, a CPU, and or a bus.
In some embodiments, the packaged IC device may include the DSP
and/or CPU or the packaged IC device may include some DSP and/or
CPU functionality.
[0104] FIG. 8A depicts an example embodiment of a plan view of an
IC device 820 that includes two TX antennas 844 and four RX
antennas 846 as well as some of the components from the RF
front-end and the digital baseband (not shown) as described above
with regard to FIGS. 5-7. In FIG. 8A, the outer footprint of the IC
device represents a packaged IC device 822 and the inner footprint
(as represented by the dashed box 824) represents a semiconductor
substrate that includes circuits that are fabricated into the
semiconductor substrate to conduct and process electrical signals
that are transmitted by the TX antennas and/or received by the RX
antennas. In the embodiment of FIG. 8A, the packaged IC device has
dimensions of 5 mm.times.5 mm (e.g., referred to as the device
"footprint") and the semiconductor substrate has a footprint that
is slightly smaller than the footprint of the packaged IC device,
e.g., the semiconductor substrate has dimensions of approximately
0.1-1 mm less than the packaged IC device on each side. Although
not shown, in an example embodiment, the packaged IC device has a
thickness of approximately 0.3-2 mm and the semiconductor substrate
has a thickness in the range of about 0.1-0.7 mm. In an embodiment,
the TX and RX antennas are designed for millimeter range radio
waves, for example, radio waves of 122-126 GHz have wavelengths in
the range of 2.46 to 2.38 mm. In FIG. 8A, the TX and RX antennas
are depicted as square boxes of approximately 1 mm.times.1 mm and
the antennas are all attached on the same planar surface of the IC
device package. For example, the antennas are attached on the top
surface of the IC package (e.g., on top of a ceramic package
material) directly above the semiconductor substrate with
conductive vias that electrically connect a conductive pad of the
semiconductor substrate to a transmission line of the antenna.
Although the TX and RX antennas may not be square, the boxes
correspond to an approximate footprint of the TX and RX antennas.
In an embodiment, the antennas are microstrip patch antennas and
the dimensions of the antennas are a function of the wavelength of
the radio waves. Other types of antennas such as dipole antennas
are also possible. FIG. 8B depicts an embodiment of a microstrip
patch antenna 830 that can be used for the TX and/or RX antennas
844 and 846 of the IC device of FIG. 8A. As shown in FIG. 8B, the
microstrip patch antenna has a patch portion 832 (with dimensions
length (L) x width (W)) and a microstrip transmission line 834. In
some embodiments, microstrip patch antennas have length and width
dimensions of one-half the wavelength of the target radio waves.
Thus, microstrip patch antennas designed for radio waves of 122-126
GHz (e.g., wavelengths in the range of 2.46 to 2.38 mm), the patch
antennas may have length and width dimensions of around 1.23-1.19
mm, but no more than 1.3 mm. It is noted that because antenna size
is a function of wavelength, the footprint of the antennas shown in
FIGS. 8A and 8B can be made to be around one-half the size of
antennas designed for radio waves around 60 GHz (e.g., wavelength
of approximately 5 mm). Additionally, the small antenna size of the
antennas shown in FIGS. 8A and 8B makes it advantageous to attach
all six of the antennas to the top surface of the package of the IC
device within the footprint of the semiconductor substrate, which
makes the packaged IC device more compact than known devices such
as the "Soli" device. That is, attaching all of the TX and RX
antennas within the footprint of the semiconductor substrate (or
mostly within the footprint of the semiconductor substrate, e.g.,
greater than 90% within the footprint).
[0105] In an embodiment, the RX antennas form a phased antenna
array and for the application of health monitoring it is desirable
to have as much spatial separation as possible between the RX
antennas to improve overall signal quality by obtaining unique
signals from each RX antenna. For example, spatial separation of
the RX antennas enables improved depth discrimination to isolate
signals that correspond to reflections from blood in a vein from
reflections from other anatomical features. Thus, as shown in FIG.
8A, the RX antennas 846 are located at the corners of the
rectangular shaped IC device. For example, the RX antennas are
located flush with the corners of the semiconductor substrate 824
and/or flush with the corners of the IC device package or within
less than about 0.5 mm from the corners of the semiconductor
substrate 824 and/or from the corners of the IC device package.
Although the IC device shown in FIG. 8A has dimensions of 5
mm.times.5 mm, IC devices having smaller (e.g., approximately 3
mm.times.3 mm) or larger dimensions are possible. In an embodiment,
the IC device has dimensions of no more than 7 mm.times.7 mm.
[0106] In the embodiment of FIG. 8A, the TX antennas 844 are
located on opposite sides of the IC chip approximately in the
middle between the two RX antennas 846 that are on the same side.
As shown in FIG. 8A, the TX antenna on the left side of the IC
device is vertically aligned with the two RX antennas on the left
side of the IC device and the TX antenna on the right side of the
IC device is vertically aligned with the two RX antennas on the
right side of the IC device. Although one arrangement of the TX and
RX antennas is shown in FIG. 8A, other arrangements are
possible.
[0107] At extremely high frequencies (e.g., 30-300 GHz) conductor
losses can be very significant. Additionally, conductor losses at
extremely high frequencies are known to be frequency-dependent,
with higher frequencies exhibiting higher conductor losses. In many
health monitoring applications, power, such as battery power, is a
limited resource that must be conserved. Additionally, for reasons
as described above such as limiting undesired reflections, low
power transmissions may be desirable for health monitoring reasons.
Because of the low power environment, conductor losses can severely
impact performance of the sensor system. For example, significant
conductor losses can occur between the antennas and the conductive
pads of the semiconductor substrate, or "die," and between the
conductive pads and the transmit/receive components in the die,
e.g., the channel-specific circuits such as amplifiers, filters,
mixers, etc. In order to reduce the impact of conductor losses in
the sensor system, it is important to locate the antennas as close
to the channel-specific transmit/receive components of the die as
possible. In an embodiment, the transmit and receive components are
strategically fabricated on the semiconductor substrate in
locations that correspond to the desired locations of the antennas.
Thus, when the TX and RX antennas are physically and electrically
attached to the IC device, the TX and RX antennas are as close as
possible to the transmit and receive components on the die, e.g.,
collocated such that a portion of the channel specific
transmit/receive component overlaps from a plan view perspective a
portion of the respective TX/RX antenna. FIG. 8C depicts an example
of the physical layout of circuit components on a semiconductor
substrate, such as the semiconductor substrate (die) depicted in
FIG. 8A. In the embodiment of FIG. 8C, the die 824 includes two TX
components 854, four RX components 856, shared circuits 860, and an
input/output interface (I/O) 862. In the example of FIG. 8C, each
TX component includes channel-specific circuits (not shown) such as
amplifiers, each RX component includes channel-specific circuits
(not shown) such as mixers, filters, and LNAs, and the shared
circuits include, for example, a voltage control oscillator (VCO),
a local oscillator (LO), frequency synthesizers, PLLs, BPFs,
divider(s), mixers, ADCs, buffers, digital logic, a DSP, CPU, or
some combination thereof that may be utilized in conjunction with
the channel-specific TX and RX components. As shown in FIG. 8C, the
transmit and receive components 854 and 856 each include an
interface 864 (such as a conductive pad) that provides an
electrical interface between the circuits on the die and a
corresponding antenna. FIG. 8D depicts a packaged IC device 822
similar to the packaged IC device shown in FIG. 8A superimposed
over the semiconductor substrate 824 shown in FIG. 8C. FIG. 8D
illustrates the locations of the TX and RX antennas 844 and 846
relative to the transmit and receive components 854 and 856 of the
die (from a plan view perspective). As illustrated in FIG. 8D, the
TX and RX antennas 844 and 846 are located directly over the
interfaces 864 of the corresponding transmit and receive components
854 and 856. In an embodiment in which the antennas are attached to
a top surface of the package (which may be less than 0.5 mm thick),
the antennas can be connected to the interface of the respective
transmit/receive components by a distance that is a fraction of a
millimeter. In an embodiment, a via that is perpendicular to the
plane of the die connects the interface of the transmit/receive
component to a transmission line of the antenna. More than one via
may be used when the antenna has more than one transmission line.
Such a collocated configuration enables the desired distribution of
the TX and RX antennas to be maintained while effectively managing
conductor losses in the system. Such a close proximity between
antennas and channel-specific circuits of the die is extremely
important at frequencies in the 122-126 GHz range and provides an
improvement over sensor systems that include conductive traces of
multiple millimeters between the antennas and the die.
[0108] Although the example of FIGS. 8A-8D shows the antennas
within the footprint of the packaged IC device 822, in some other
embodiments, the antennas may extend outside the footprint of the
die and/or the packaged IC device while still being collocated with
the corresponding transmit/receive components on the die. For
example, the antennas may be dipole antennas that have portions of
the antennas that extend outside the footprint of the die and/or
the packaged IC device.
[0109] It has been realized that for the application of monitoring
a health parameter such as the blood glucose level in the blood of
a person, it is important that the TX antennas are able to
illuminate at least one vein near the skin of the person. In order
for a TX antenna to illuminate at least one vein near the skin of
the person, it is desirable for at least one of the antennas to be
spatially close to a vein. Because of variations in the locations
of veins relative to the location of the monitoring system (e.g., a
smartwatch), it has been found that a transverse configuration of
the TX antennas relative to the expected location of a vein or
veins provides desirable conditions for monitoring a health
parameter such as the blood glucose level in the blood of a person.
When the wearable device is worn on a portion of a limb such as the
wrist, the TX antennas are distributed in a transverse
configuration relative to the limb and relative to the expected
location of a vein or veins that will be illuminated by the TX
antennas.
[0110] FIG. 9 depicts an IC device 922 similar to that of FIG. 8A
overlaid on the hand/wrist 912 that is described above with
reference to FIG. 2A-2C. The IC device is oriented with regard to
the basilic and cephalic veins 914 and 916 such that the two TX
antennas 944 are configured transverse to the basilic and cephalic
veins. That is, the two TX antennas are distributed transversely
relative to the orientation (e.g., the linear direction) of the
vessel or vessels that will be monitored, such as the basilic and
cephalic veins. For example, in a transverse configuration, a
straight line that passes through the two TX antennas would be
transverse to the vessel or vessels that will be monitored, such as
the basilic and cephalic veins. In an embodiment in which the
wearable device is worn on the wrist, the transverse configuration
of the TX antennas is such that a line passing through both of the
TX antennas is approximately orthogonal to the wrist and
approximately orthogonal to the orientation of the vessel or
vessels that will be monitored, such as the basilic and cephalic
veins. For example, a line passing through both of the TX antennas
and the orientation of the vessel or vessels that will be
monitored, such as the basilic and cephalic veins, may be without
about 20 degrees from orthogonal.
[0111] FIG. 10 depicts an IC device 1022 similar to that of FIG. 8A
overlaid on the back of the smartwatch 1000 described above with
reference to FIGS. 1A and 1B. As shown in FIGS. 9 and 10, the two
TX antennas are configured such that when the smartwatch is worn on
the wrist, the two TX antennas are transverse to veins such as the
basilic and cephalic veins that run parallel to the length of the
arm and wrist.
[0112] FIGS. 11 and 12 are provided to illustrate the expanded
illumination volume that can be achieved by a sensor system 1010
that includes a transverse
[0113] TX antenna configuration. FIG. 11 depicts a side view of a
sensor system in a case in which the two TX antennas 1044 are
configured parallel to veins such as the basilic and cephalic veins
of a person wearing the smartwatch 1000. In the view shown in FIG.
11, the two TX antennas are in-line with each other such that only
one of the two TX antennas is visible from the side view. When the
TX antennas transmit millimeter range radio waves, the
electromagnetic energy may have a two-dimensional (2D) illumination
pattern as illustrated by dashed line 1020. Given the
two-dimensional pattern as illustrated in FIG. 11, the two TX
antennas illuminate an area that has a maximum width in the
transverse direction (transverse to veins that run parallel to the
length of the arm and wrist and referred to herein as the
transverse width) identified by arrow 1022. Although the
illumination pattern is described and illustrated in two dimensions
(2D), it should be understood that illumination actually covers a
3D space or volume.
[0114] FIG. 12 depicts the same side view as shown in FIG. 11 in a
case in which the two TX antennas 1044 are configured transverse to
veins such as the basilic and cephalic veins of a person wearing
the smartwatch 1000. In the view shown in FIG. 12, the two TX
antennas are spatially separated from each other such that both of
the TX antennas are visible from the side view. When the TX
antennas transmit millimeter range radio waves, the electromagnetic
energy may have a 2D illumination pattern as illustrated by dashed
lines 1024. Given the 2D elimination patterns of the two TX
antennas, the two TX antennas combine to illuminate an area that
has a width in the transverse direction (transverse width)
identified by arrow 1026, which is wider than the transverse width
for the TX antenna configuration shown in FIG. 11 (e.g., almost
twice as wide). A wider illumination area improves the coverage
area for the sensor system 1010 and increases the likelihood that
the sensor system will illuminate a vein in the person wearing the
smartwatch. An increased likelihood that a vein is illuminated can
provide more reliable feedback from the feature of interest (e.g.,
blood in the vein) and thus more reliable monitoring results.
Additionally, a wider illumination area can increase the power of
the radio waves that illuminate a vein, resulting in an increase in
the power of the electromagnetic energy that is reflected from the
vein, which can improve the quality of the received signals.
[0115] It has been established that the amount of glucose in the
blood (blood glucose level) affects the reflectivity of millimeter
range radio waves. However, when millimeter range radio waves are
applied to the human body (e.g., at or near the skin surface),
electromagnetic energy is reflected from many objects including the
skin itself, fibrous tissue such as muscle and tendons, and bones.
In order to effectively monitor a health parameter such as the
blood glucose level of a person, electrical signals that correspond
to electromagnetic energy that is reflected from blood (e.g., from
the blood in a vein) should be isolated from electrical signals
that correspond to electromagnetic energy that is reflected from
other objects such as the skin itself, fibrous tissue, and bone, as
well as from electrical signals that correspond to electromagnetic
energy that is emitted directly from the TX antennas (referred to
herein as electromagnetic energy leakage or simply as "leakage")
and received by an antenna without passing through the skin of the
person.
[0116] Various techniques that can be implemented alone or in
combination to isolate electrical signals that correspond to
reflections from blood from other electrical signals that
correspond to other reflections (such as reflections from bone
and/or fibrous tissue such as muscle and tendons) and/or signals
that correspond to leakage are described below. Such techniques
relate to and/or involve, for example, transmission
characteristics, beamforming, Doppler effect processing, leakage
mitigation, and antenna design.
[0117] As is known in the field, radar detection involves
transmitting electromagnetic energy and receiving reflected
portions of the transmitted electromagnetic energy. Techniques for
transmitting electromagnetic energy in radar systems include
impulse, chirp, and stepped frequency techniques.
[0118] FIGS. 13A-13C depict frequency versus time graphs of
impulse, chirp, and stepped frequency techniques for transmitting
electromagnetic energy in a radar system. FIG. 13A depicts a radar
transmission technique that involves transmitting pulses of
electromagnetic energy at the same frequency for each pulse,
referred to as "impulse" transmission. In the example of FIG. 13A,
each pulse is at frequency, f.sub.1, and lasts for a constant
interval of approximately 2 ns. The pulses are each separated by
approximately 2 ns.
[0119] FIG. 13B depicts a radar transmission technique that
involves transmitting pulses of electromagnetic energy at an
increasing frequency for each interval, referred to herein as
"chirp" transmission. In the example of FIG. 13B, each chirp
increases in frequency from frequency f.sub.0 to f.sub.1 over an
interval of 2 ns and each chirp is separated by 2 ns. In other
embodiments, the chirps may be separated by very short intervals
(e.g., a fraction of a nanosecond) or no interval.
[0120] FIG. 13C depicts a radar transmission technique that
involves transmitting pulses of electromagnetic energy at the same
frequency during a particular pulse but at an increased frequency
from pulse-to-pulse, referred to herein as a "stepped frequency"
transmission or a stepped frequency pattern. In the example of FIG.
13C, each pulse has a constant frequency over the interval of the
pulse (e.g., over 2 ns), but the frequency increases by an
increment of .DELTA.f from pulse-to-pulse. For example, the
frequency of the first pulse is f.sub.0, the frequency of the
second pulse is f.sub.0+.DELTA.f, the frequency of the third pulse
is f.sub.0+2.DELTA.f, and the frequency of the fourth pulse is
f.sub.0+3.DELTA.f, and so on.
[0121] In an embodiment, the sensor system described herein is
operated using stepped frequency transmission. Operation of the
sensor system using stepped frequency transmission is described in
more detail below. FIG. 14 depicts a burst of electromagnetic
energy using stepped frequency transmission. The frequency of the
pulses in the burst can be expressed as:
f.sub.n=f.sub.0+n.DELTA.f
where f.sub.0=starting carrier frequency, .DELTA.f=step size,
.tau.=pulse length (active, per frequency), T=repetition interval,
n=1, . . . N, each burst consists of N pulses (frequencies) and a
coherent processing interval (CPI)=NT=1 full burst.
[0122] Using stepped frequency transmission enables relatively high
range resolution. High range resolution can be advantageous when
trying to monitor a health parameter such as the blood glucose
level in a vein that may, for example, have a diameter in the range
of 1-4 mm. For example, in order to effectively isolate a signal
that corresponds to reflections of electromagnetic energy from the
blood in a 1-4 mm diameter vein, it is desirable to have a high
range resolution, which is provided by the 122-126 GHz frequency
range.
[0123] Using stepped frequency transmission, range resolution can
be expressed as:
.DELTA.R=c/2B
wherein c=speed of light, B=effective bandwidth. The range
resolution can then be expressed as:
.DELTA.R=c/2N.DELTA.f
wherein B=.DELTA.f. Thus, range resolution does not depend on
instantaneous bandwidth and the range resolution can be increased
arbitrarily by increasing .DELTA.f.
[0124] In an embodiment, the electromagnetic energy is transmitted
from the TX antennas in the frequency range of approximately
122-126 GHz, which corresponds to a total bandwidth of
approximately 4 GHz, e.g., B=4 GHz. FIG. 15A depicts a graph of the
transmission bandwidth, B, of transmitted electromagnetic energy in
the frequency range of 122-126 GHz. Within a 4 GHz bandwidth, from
122-126 GHz, discrete frequency pulses can be transmitted. For
example, in an embodiment, the number of discrete frequencies that
can be transmitted ranges from, for example, 64-256 discrete
frequencies. In a case with 64 discrete frequency pulses and a
repetition interval, T, over 4 GHz of bandwidth, the step size,
.DELTA.f, is 62.5 MHz (e.g., 4 GHz of bandwidth divided by 64=62.5
MHz) and in a case with 256 discrete frequency pulses and a
repetition interval, T, over 4 GHz of bandwidth, the step size,
.DELTA.f, is 15.625 MHz (e.g., 4 GHz of bandwidth divided by
256=15.625 MHz). FIG. 15B depicts a graph of stepped frequency
pulses that have a repetition interval, T, and a step size,
.DELTA.f, of 62.5 MHz (e.g., 4 GHz of bandwidth divided by 64=62.5
MHz). As described above, an example sensor system has four RX
antennas. Assuming a discrete frequency can be received on each RX
antenna, degrees of freedom (DOF) of the sensor system in the
receive operations can be expressed as: 4 RX antennas.times.64
discrete frequencies=256 DOF; and 4 RX antennas.times.256 discrete
frequencies=1K DOF. The number of degrees of freedom (also referred
to as "transmission frequency diversity") can provide signal
diversity, which can be beneficial in an environment such as the
anatomy of a person. For example, the different discrete
frequencies may have different responses to the different
anatomical features of the person. Thus, greater transmission
frequency diversity can translate to greater signal diversity, and
ultimately to more accurate health monitoring.
[0125] One feature of a stepped frequency transmission approach is
that the sensor system receives reflected electromagnetic energy at
basically the same frequency over the repetition interval, T. That
is, as opposed to chirp transmission, the frequency of the pulse
does not change over the interval of the pulse and therefore the
received reflected electromagnetic energy is at the same frequency
as the transmitted electromagnetic energy for the respective
interval. FIG. 16A depicts a frequency versus time graph of
transmission pulses, with transmit (TX) interval and receive (RX)
intervals identified relative to the pulses. As illustrated in FIG.
16A, RX operations for the first pulse occur during the pulse
length, .tau., of repetition interval, T, and during the interval
between the next pulse. FIG. 16B depicts an amplitude versus time
graph of the transmission waveforms that corresponds to FIG. 16A.
As illustrated in FIG. 16B, the amplitude of the pulses is constant
while the frequency increases by .DELTA.f at each repetition
interval, T.
[0126] In an embodiment, the power of the transmitted
electromagnetic energy can be set to achieve a desired penetration
depth and/or a desired illumination volume. In an embodiment, the
transmission power from the TX antennas is about 15 dBm.
[0127] In an embodiment, electromagnetic energy can be transmitted
from the TX antennas one TX antenna at a time (referred to herein
as "transmit diversity"). For example, a signal is transmitted from
a first one of the two TX antennas while the second one of the two
TX antennas is idle and then a signal is transmitted from the
second TX antenna while the first TX antenna is idle. Transmit
diversity may reveal that illumination from one of the two TX
antennas provides a higher quality signal than illumination from
the other of the two TX antennas. This may be especially true when
trying to illuminate a vein whose location may vary from person to
person and/or from moment to moment (e.g., depending on the
position of the wearable device relative to the vein). Thus,
transmit diversity can provide sets of received signals that are
independent of each other and may have different characteristics,
e.g., signal power, SNR, etc.
[0128] Some theory related to operating the sensor system using a
stepped frequency approach is described with reference to FIG. 17,
which illustrates operations related to transmitting, receiving,
and processing phases of the sensor system operation. With
reference to the upper portion of FIG. 17, a time versus amplitude
graph of a transmitted signal burst, similar to the graph of FIG.
16B, is shown. The graph represents the waveforms of five pulses of
a burst at frequencies of f.sub.0, f.sub.0+.DELTA.f,
f.sub.0+2.DELTA.f, f.sub.0+3.DELTA.f, and f.sub.0+4.DELTA.f.
[0129] The middle portion of FIG. 17 represents values of received
signals that correspond to the amplitude, phase, and frequency of
each pulse in the burst of four pulses. In an embodiment, received
signals are placed in range bins such that there is one complex
sample per range bin per frequency. Inverse Discrete Fourier
Transforms (IDFTs) are then performed on a per-range bin basis to
determine range information. The bottom portion of FIG. 17
illustrates an IDFT process that produces a signal that corresponds
to the range of a particular object. For example, the range may
correspond to a vein such as the basilic vein. In an embodiment,
some portion of the signal processing is performed digitally by a
DSP or CPU. Although one example of a signal processing scheme is
described with reference to FIG. 17, other signal processing
schemes may be implemented to isolate signals that correspond to
reflections from blood in a vein (such as the basilic vein) from
signals that correspond to reflections from other undesired
anatomical features (such as tissue and bones) and from signals
that correspond to leakage from the TX antennas.
[0130] Beamforming is a signal processing technique used in sensor
arrays for directional signal transmission and/or reception.
Beamforming can be implemented by combining elements in a phased
antenna array in such a way that signals at particular angles
experience constructive interference while other signals experience
destructive interference. Beamforming can be used in both transmit
operations and receive operations in order to achieve spatial
selectivity, e.g., to isolate some received signals from other
received signals. In an embodiment, beamforming techniques are
utilized to isolate signals that correspond to reflections from
blood in a vein (such as the basilic vein) from signals that
correspond to reflections from other undesired anatomical features
(such as tissue and bones) and from signals that correspond to
leakage from the TX antennas. An example of the concept of
beamforming as applied to blood glucose monitoring using a wearable
device such as a smartwatch is illustrated in FIG. 18. In
particular, FIG. 18 depicts an expanded view of the anatomy of a
wrist, similar to that described above with reference to FIGS.
2A-4D, relative to RX antennas 1846 of a sensor system 1810 that is
integrated into a wearable device such as a smartwatch 1800. The
anatomical features of the wrist that are illustrated in FIG. 18
include the skin 1822, a vein such as the basilic vein 1816, the
radius bone 1834, and the ulna bone 1836. FIG. 18 also illustrates
2D representations of reception beams 1850 (although it should be
understood that the beams occupy a 3D space/volume) that correspond
to electromagnetic energy that is reflected from the blood in the
basilic vein to the respective RX antenna.
[0131] In an embodiment, a beamforming technique involves
near-field beamforming, where each RX antenna of the phased antenna
array is steered independently to a different angle as opposed to
far-field beamforming where all of the antennas in a phased antenna
array are steered collectively to the same angle. For example,
near-field beamforming is used when the target is less than about
4-10 wavelengths from the phased antenna array. In the case of a
sensor system operating at 122-126 GHz, 4-10 wavelengths is
approximately within about 10-25 mm from the phased antenna array.
In the case of monitoring a health parameter related to blood, the
blood vessels that are monitored (e.g., the basilic and/or cephalic
veins) are likely to be less than 10-25 mm from the phase antenna
array. Thus, in an embodiment, near-field beamforming techniques
are used to isolate desired signals (e.g., signals that correspond
to reflections from blood in a vein such as the basilic vein) from
undesired signals (e.g., signals that correspond to reflections
from other undesired anatomical features, such as tissue and bones,
and from signals that correspond to leakage from the TX antennas).
Beamforming can be accomplished in digital, in analog, or in a
combination of digital and analog signal processing. In an
embodiment, the ranging technique described above, which utilizes
stepped frequencies, is used in combination with beamforming to
isolate signals that correspond to the reflection of
electromagnetic energy from the basilic vein.
[0132] The Doppler effect relates to the change in frequency or
wavelength of a wave (e.g., an electromagnetic wave) in relation to
an observer, which is moving relative to the source of the wave.
The Doppler effect can be used to identify fluid flow by sensing
the shift in wavelength of reflections from particles moving with
the fluid flow. In accordance with an embodiment of the invention,
signal processing based on the Doppler effect is applied to signals
received by the sensor system to isolate signals that correspond to
reflections from flowing blood from signals that correspond to
reflections from objects that are stationary, at least with respect
to the flowing blood. As described above, millimeter wave radio
waves are transmitted below the skin to illuminate anatomical
features below the skin. In the area of the body around the wrist,
blood flowing through veins such as the basilic and cephalic veins
is moving relative to the other anatomical features in the area.
Thus, Doppler effect theory and corresponding signal processing is
used to filter for those signals that correspond to movement
(movement relative to other signals that correspond to stationary
objects). In the health monitoring application as described herein,
the signals that correspond to the flowing blood can be identified
by applying the Doppler effect theory to the signal processing to
isolate the signals that correspond to the flowing blood. The
isolated signals can then be used to measure a health parameter
such as blood glucose level.
[0133] FIG. 19 illustrates an IC device 1922 similar to the IC
device 822 shown in FIG. 8A relative to a vein 1916 such as the
basilic or cephalic vein in the wrist area of a person. FIG. 19
also illustrates the flow of blood through the vein relative to the
IC device. Because the blood is moving relative to the TX and RX
antennas 1944 and 1946 of the sensor system, Doppler effect theory
can be applied to signal processing of the received signals to
isolate the signals that correspond to the flowing blood from the
signals that correspond to objects that are stationary relative to
the flowing blood. For example, received signals that correspond to
flowing blood are isolated from received signals that correspond to
stationary objects such as bone and fibrous tissue such as muscle
and tendons. In an embodiment, Doppler processing involves
performing a fast Fourier transform (FFT) on samples to separate
the samples into component Doppler shift frequency bins. Frequency
bins that represent no frequency shift can be ignored (as they
correspond to reflections from stationary objects) and frequency
bins that represent a frequency shift (which corresponds to
reflections from a moving object) can be used to determine a health
parameter. That is, Doppler effect processing can be used to
isolate signals that represent no frequency shift (as they
correspond to reflections from stationary objects) from frequency
bins that represent a frequency shift (which correspond to
reflections from a moving object). In an embodiment, Doppler effect
signal processing may involve sampling over a relatively long
period of time to achieve small enough velocity bins to decipher
relative movement. Thus, Doppler effect theory and corresponding
signal processing can be used to filter for only those signals that
correspond to movement (movement relative to the other received
signals). Such an approach allows signals that correspond to
reflections from flowing blood, e.g., blood in a vein, to be
isolated from other signals, e.g., signals that correspond to
stationary object. In an embodiment, Doppler signal processing is
performed digitally by a DSP and/or by a CPU.
[0134] With reference to FIG. 8A, during operation of the IC device
822, some electromagnetic energy that is emitted from the TX
antennas 844 will be received directly by at least one of the RX
antennas 846 without first passing through the skin of the person.
Signals that correspond to such electromagnetic energy do not
correspond to a health parameter that is to be monitored and are
referred to herein as electromagnetic energy leakage or simply as
"leakage." In an embodiment, various signal processing techniques
may be implemented to mitigate the effects of leakage. For example,
signals that correspond to leakage should be isolated from signals
that correspond to reflections of radio waves from blood in a vein.
In an embodiment, leakage is mitigated by applying signal
processing to implement beamforming, Doppler effect processing,
range discrimination or a combination thereof. Other techniques
such as antenna design and antenna location can also be used to
mitigate the effects of leakage.
[0135] In an embodiment, signal processing to isolate signals that
correspond to reflections of radio waves from blood in a vein from
signals that correspond to reflections of radio waves from other
anatomical objects (such as bone and fibrous tissue such as muscle
and tendons) and from signals that correspond to leakage can be
implemented in part or in full digitally by a DSP. FIG. 20 is an
embodiment of a DSP 2064 that includes a Doppler effect component
2070, a beamforming component 2072, and a ranging component 2074.
In an embodiment, the Doppler effect component is configured to
implement digital Doppler effect processing, the beamforming
component is configured to implement digital beamforming, and the
ranging component is configured to implement digital ranging.
Although the DSP is shown as including the three components, the
DSP may include fewer components and the DSP may include other
digital signal processing capability. The DSP may include hardware,
software, and/or firmware or a combination thereof that is
configured to implement the digital signal processing that is
described herein. In an embodiment, the DSP may be embodied as an
ARM processor (Advanced RISC (reduced instruction set computing)
Machine). In some embodiments, components of a DSP can be
implemented in the same IC device as the RF front-end and the TX
and RX antennas. In other embodiments, components of the DSP are
implemented in a separate IC device or IC devices.
[0136] In an embodiment, the transmission of millimeter radio waves
and the processing of signals that correspond to received radio
waves is a dynamic process that operates to locate signals
corresponding to the desired anatomy (e.g., signals that correspond
to reflections of radio waves from a vein) and to improve the
quality of the desired signals (e.g., to improve the SNR). For
example, the process is dynamic in the sense that the process is an
iterative and ongoing process as the location of the sensor system
relative to a vein or veins changes.
[0137] Although the techniques described above are focused on
monitoring the blood glucose level in a person, the disclosed
techniques are also applicable to monitoring other parameters of a
person's health such as, for example, blood pressure and heart
rate. For example, the reflectively of blood in a vessel such as
the basilic vein will change relative to a change in blood
pressure. The change in reflectivity as monitored by the sensor
system can be correlated to a change in blood pressure and
ultimately to an absolute value of a person's blood pressure.
Additionally, monitored changes in blood pressure can be correlated
to heart beats and converted over time to a heart rate, e.g., in
beats per minute. In other embodiments, the disclosed techniques
can be used to monitor other parameters of a person's health that
are affected by the chemistry of the blood. For example, the
disclosed techniques may be able to detect changes in blood
chemistry that correspond to the presence of foreign chemicals such
as alcohol, narcotics, cannabis, etc. The above-described
techniques may also be able to monitor other parameters related to
a person, such as biometric parameters.
[0138] In an embodiment, health monitoring using the techniques
described above, may involve a calibration process. For example, a
calibration process may be used for a particular person and a
particular monitoring device to enable desired monitoring
quality.
[0139] The above-described techniques are used to monitor a health
parameter (or parameters) related to blood in a blood vessel or in
blood vessels of a person. The blood vessels may include, for
example, arteries, veins, and/or capillaries. The health monitoring
technique can target blood vessels other than the basilic and/or
cephalic veins. For example, other near-surface blood vessels
(e.g., blood vessels in the subcutaneous layer) such as arteries
may be targeted. Additionally, locations other than the wrist area
can be targeted for health monitoring. For example, locations in
around the ear may be a desirable location for health monitoring,
including, for example, the superficial temporal vein and/or artery
and/or the anterior auricular vein or artery. In an embodiment, the
sensor system may be integrated into a device such as a hearing aid
or other wearable device that is attached to the ear or around or
near the ear. In another embodiment, locations in and around the
elbow joint of the arm may be a desirable location for health
monitoring. For example, in or around the basilica vein or the
cephalic vein at or near the elbow.
[0140] Although the techniques are described as using a frequency
range of 122-126 GHz, some or all of the above-described techniques
may be applicable to frequency ranges other than 122-126 GHz. For
example, the techniques may be applicable to frequency ranges
around 60 GHz. In another embodiment, the techniques described
herein may be applicable to the 2-6 GHz frequency range. For
example, a system similar to that described with reference to FIG.
6 may be used to implement health monitoring by transmitting and
receiving RF energy in the 2-6 GHz range. In still another
embodiment, multiple non-contiguous frequency ranges may be used to
implement health monitoring. For example, health monitoring may be
implemented using both the 2-6 GHz frequency range and the 122-126
GHz frequency range. For example, in an embodiment, stepped
frequency scanning in implemented in the lower frequency range and
then in the higher frequency range, or vice versa. Using multiple
non-contiguous frequency ranges (e.g., both the 2-6 GHz frequency
range and the 122-126 GHz frequency range) may provide improved
accuracy of health monitoring.
[0141] In an embodiment, the sensor system may be embedded into a
different location in a monitoring device. For example, in an
embodiment, a sensor system (or a portion of the sensor system such
as IC device as shown in FIG. 8A) is embedded into an attachment
device such as the strap of a smartwatch so that the sensor system
can target a different blood vessel in the person. For example, the
sensor system may be embedded into the strap of a smartwatch so
that a blood vessel at the side area of the wrist and/or at the
anterior area of the wrist can be monitored. In such an embodiment,
the strap may include conductive signal paths that communicate
signals between the sensor IC device and the processor of the
smartwatch.
[0142] FIG. 21 is a process flow diagram of a method for monitoring
a health parameter in a person. At block 2102, millimeter range
radio waves are transmitted over a three-dimensional (3D) space
below the skin surface of a person. At block 2104, radio waves are
received on multiple receive antennas, the received radio waves
including a reflected portion of the transmitted radio waves.
[0143] At block 2106, a signal is isolated from a particular
location in the 3D space in response to receiving the radio waves
on the multiple receive antennas. At block 2108, a signal that
corresponds to a health parameter in the person is output in
response to the isolated signal. In an embodiment, the health
parameter is blood glucose level. In other embodiments, the health
parameter may be blood pressure or heart rate.
[0144] In an embodiment, health monitoring information that is
gathered using the above-described techniques can be shared. For
example, the health monitoring information can be displayed on a
display device and/or transmitted to another computing system via,
for example, a wireless link.
[0145] As mentioned above, locations in around the ear may be
desirable for health monitoring, including, for example, the
superficial temporal artery or vein, the anterior auricular artery
or vein, and/or the posterior auricular artery. FIG. 22A depicts a
side view of the area around a person's ear 2200 with the typical
approximate locations of veins and arteries, including the
superficial temporal artery 2202, the superficial temporal vein
2204, the anterior auricular artery 2206 and vein 2208, the
posterior auricular artery 2210, the occipital artery 2212, the
external carotid artery 2214, and the external jugular vein 2216.
In an embodiment, a sensor system, such as the sensor system
described herein, may be integrated into a device such as a hearing
aid or another wearable device that is attached to the ear or
around or near the ear. FIG. 22B depicts an embodiment of system
2250 in which at least elements of an RF front-end 2222 (including
the transmit and receive antennas and corresponding transmit and
receive components as shown in FIGS. 5-7) are located separate from
a housing 2252 that includes, for example, a digital processor,
wireless communications capability, and a source of electric power,
all of which are enclosed within the housing. For example,
components of the digital baseband system as shown in FIG. 5 may be
enclosed within the housing and the housing is connected to the RF
front-end by a communications medium 2254, such as a conductive
wire or wires. In an embodiment, the housing 2252 is worn behind
the ear 2200 similar to a conventional hearing aid and the RF
front-end 2222 is located near a blood vessel that is around the
ear. For example, the RF front-end may include adhesive material
that enables the RF front-end to be adhered to the skin near a
blood vessel such as, for example, the superficial temporal artery
2202 or vein 2204, the anterior auricular artery 2206 or vein 2208,
and/or the posterior auricular artery 2210. FIG. 22C illustrates
how a device, such as the device depicted in FIG. 22B, may be worn
near the ear 2200 of a person similar to how a conventional hearing
aid is worn. FIG. 22C also shows the RF front-end 2222 relative to
the superficial temporal artery 2202 and the superficial temporal
vein 2204 as shown in FIG. 22C. In an embodiment, the sensor system
may be integrated with a conventional hearing aid to provide both
hearing assistance and health monitoring. For example, the
integrated system may include a housing, a speaker that is inserted
into the ear, and an RF front-end that is attached to the skin
around the ear and near to a blood vessel. In other embodiments, a
sensor system may be integrated into ear buds or into some other
type of device that is worn around or near the ear.
[0146] Although the magnitude of the reflected RF energy (also
referred to as amplitude) that is received by the sensor system has
been found to correspond to a health parameter, such as blood
glucose level, it has further been found that the combination of
the amplitude and the phase of the reflected RF energy can provide
improved correspondence to a health parameter, such as a blood
glucose level. Thus, in an embodiment, a value that corresponds to
a health parameter of a person is generated in response to
amplitude and phase data that is generated in response to received
radio waves. For example, the value that corresponds to a health
parameter may be a value that indicates a blood glucose level in
mg/dL or some other indication of the blood glucose level, a value
that indicates a person's heart rate (e.g., in beats per minute),
and/or a value that indicates a person's blood pressure (e.g., in
millimeters of mercury, mmHg). In an embodiment, a method for
monitoring a health parameter (e.g., blood glucose level) in a
person involves transmitting radio waves below the skin surface of
a person and across a range of stepped frequencies, receiving radio
waves on a two-dimensional array of receive antennas, the received
radio waves including a reflected portion of the transmitted radio
waves across the range of stepped frequencies, generating data that
corresponds to the received radio waves, wherein the data includes
amplitude and phase data across the range of stepped frequencies,
and determining a value that is indicative of a health parameter in
the person in response to the amplitude and phase data. In an
embodiment, the phase data corresponds to detected shifts in sine
waves that are received at the sensor system. In another
embodiment, a value that is indicative of a health parameter in the
person may be determined in response to phase data but not in
response to amplitude data.
[0147] Additionally, it has been found that certain step sizes in
stepped frequency scanning can provide good correspondence in
health parameter monitoring. In an embodiment, the frequency range
that is scanned using stepped frequency scanning is on the order of
100 MHz in the 122-126 GHz range and the step size is in the range
of 100 kHz-1 MHz. For example, in an embodiment, the step size over
the scanning range is around 100 kHz (.+-.10%).
[0148] Although the amplitude and phase of the reflected RF energy
that is received by the sensor system has been found to correspond
to a health parameter, such as blood glucose level, it has further
been found that the combination of the amplitude and phase of the
reflected RF energy and some derived data, which is derived from
the amplitude and/or phase data, can provide improved
correspondence to a health parameter, such as blood glucose level.
Thus, in an embodiment, some data is derived from the amplitude
and/or phase data that is generated by the sensor system in
response to the received RF energy and the derived data is used,
often in conjunction with the amplitude and/or phase data, to
determine a value that corresponds to a health parameter (e.g., the
blood glucose level) of a person. For example, the data derived
from the amplitude and/or phase data may include statistical data
such as the standard deviation of the amplitude over a time window
and/or the standard deviation of the phase over a time window. In
an embodiment, data can be derived from the raw data on a
per-receive antenna basis or aggregated amongst the set of receive
antennas. In a particular example, it has been found that the
amplitude, phase, and the standard deviation of amplitude over a
time window (e.g., a time window of 1 second) corresponds well to
blood glucose levels.
[0149] In an embodiment, a method for monitoring a health parameter
(e.g., blood glucose level) in a person involves transmitting radio
waves below the skin surface of the person and across a range of
stepped frequencies, receiving radio waves on a two-dimensional
array of receive antennas, the received radio waves including a
reflected portion of the transmitted radio waves across the range
of stepped frequencies, generating data that corresponds to the
received radio waves, wherein the data includes amplitude and phase
data, deriving data from at least one of the amplitude and phase
data, and determining a value that is indicative of a health
parameter in the person in response to the derived data. In an
embodiment, the value is determined in response to not only the
derived data but also in response to the amplitude data and the
phase data. In an embodiment, the derived data is a statistic that
is derived from amplitude and/or phase data that is generated over
a time window. For example, the statistic is one of a standard
deviation, a moving average, and a moving mean. In other
embodiments, the derived data may include multiple statistics
derived from the amplitude and/or phase data. In an embodiment, a
value that is indicative of a health parameter is determined in
response to a rich set of parameters associated with the stepped
frequency scanning including the scanning frequency, the detected
amplitudes and phases of the received RF energy, data derived from
the detected amplitudes and phases, the state of the transmit
components, and the state of the receive components.
[0150] Using a sensor system, such as the sensor system described
above, there are various parameters to be considered in the stepped
frequency scanning process. Some parameters are fixed during
operation of the sensor system and other parameters may vary during
operation of the sensor system. Of the parameters that may vary
during operation of the sensor system, some may be controlled and
others are simply detected. FIG. 23 is a table of parameters
related to stepped frequency scanning in a system such as the
above-described system. The table includes an identification of
various parameters and an indication of whether the corresponding
parameter is fixed during operation (e.g., fixed as a physical
condition of the sensor system) or variable during operation and if
the parameter is variable, whether the parameter is controlled, or
controllable, during operation or simply detected during operation.
In the table of FIG. 23, "Time" refers to an aspect of time such as
an absolute moment in time relative to some reference (or may refer
to a time increment, e.g., .DELTA.t). In an embodiment, the time
corresponds to all of the other parameters in the table. That is,
the state or value of all of the other parameters in the table is
the state or value at that time in the stepped frequency scanning
operation. "TX/RX frequency" refers to the transmit/receive
frequency of the sensor system at the corresponding time as
described above with reference to, for example, FIG. 6. The TX1 and
TX2 state refers to the state of the corresponding transmitter
(e.g., whether or not the corresponding power amplifiers (PAs) are
on or off) at the corresponding time. In an embodiment, RF energy
transmitted from the transmission antennas can be controlled by
activating/deactivating the corresponding PAs. The RX1 and RX2
state refers to the state of the corresponding receive paths (e.g.,
whether or not components of the corresponding receive paths are
active or inactive, which may involve powering on/off components in
the receive path) at the corresponding time. In an embodiment, the
receiving of RF energy on the receive paths can be controlled by
activating/deactivating components of the corresponding receive
paths. The RX detected amplitude refers to the amplitude of the
received signals at the corresponding receive path and at the
corresponding time and the RX detected phase refers to the phase
(or phase shift) of the received signals at the corresponding
receive path and at the corresponding time. The TX and RX antenna
2D position refers to information about the 2D position of the
antennas in the sensor system (e.g., the positions of the antennas
relative to each other or the positions of the antennas relative to
a common location) and the antenna orientation refers to antenna
characteristics that may be specific to a particular polarization
orientation. For example, a first set of antennas may be configured
for vertical polarization while a second set of antennas is
configured for horizontal polarization in order to achieve
polarization diversity. Other antenna orientations and/or
configurations are possible. As indicated in the table, antenna
position and antenna orientation are fixed during stepped frequency
scanning.
[0151] FIG. 24 is a table of parameters similar to the table of
FIG. 23 in which examples are associated with each parameter for a
given step in a stepped frequency scanning operation in order to
give some context to the table. As indicated in FIG. 24, the time
is "t1" (e.g., some absolute time indication or a time increment)
and the operating frequency is "X GHz," e.g., in the range of 2-6
GHz or 122-126 GHz. In the example of FIG. 24, TX1, RX1, and RX4
are active and TX2, RX2, and RX3 are inactive during this step in
the stepped frequency scanning operation (e.g., at time t1). The
detected amplitudes of RX1 and RX4 are indicated as "ampl1" and
"ampl4" and the detected phases of RX1 and RX4 are indicated as
"ph1" and "ph4." The detected amplitudes and phases of RX2 and RX3
are indicated as "n/a" since the receive paths are inactive. The
positions of the transmit and receive antennas are indicated in the
lower portion of the table and correspond to the configuration
described above with reference to FIGS. 8A-8D and the antenna
orientations are evenly distributed amongst vertical and horizontal
orientations so as to enable polarization diversity. FIG. 25
depicts an embodiment of the IC device 820 from FIG. 8A in which
the antenna polarization orientation is illustrated by the
orientation of the transmit and receive antennas 844 and 846,
respectively. In FIG. 25, rectangles with the long edges oriented
vertically represent a vertical polarization orientation (e.g.,
antennas TX1, RX1, and RX4) and rectangles with the long edges
oriented horizontally represent a horizontal polarization
orientation (e.g., antennas TX2, RX2, and RX3). FIG. 24 reflects
the same polarization orientations in which TX1 is configured to
vertically polarize the transmitted RF energy and RX1 and RX4 are
configured to receive vertically polarized RF energy and TX2 is
configured to horizontally polarize the transmitted RF energy and
RX2 and RX3 are configured to receive horizontally polarized RF
energy. Although FIG. 24 is provided as an example, the parameter
states of the variable parameters are expected to change during
stepped frequency scanning and the fixed parameters may be
different in different sensor system configurations.
[0152] In an embodiment, during a stepped frequency scanning
operation, certain data, referred to herein as "raw data," is
generated. For example, the raw data is generated as digital data
that can be further processed by a digital data processor. FIG. 26
is a table of raw data (e.g., digital data) that is generated
during stepped frequency scanning. The raw data depicted in FIG. 26
includes variable parameters of time, TX/RX frequency, RX1
amplitude/phase, RX2 amplitude/phase, RX3 amplitude/phase, and RX4
amplitude/phase. In the example of FIG. 26, the raw data
corresponds to a set of data, referred to as a raw data record,
which corresponds to one step in the stepped frequency scanning For
example, the raw data record corresponds to a particular frequency
pulse as shown and described above with reference to FIG. 17. In an
embodiment, a raw data record also includes some or all of the
parameters identified in FIGS. 23 and 24. For example, the raw data
record may include other variable and/or fixed parameters that
correspond to the stepped frequency scanning operation. In an
embodiment, multiple raw data records are accumulated and processed
by a digital processor, which may include a DSP, an MCU, and/or a
CPU as described above, for example, with reference to FIG. 5. Raw
data (e.g., in the form of raw data records) may be used for
machine learning.
[0153] As described above, it has been found that the combination
of the amplitude and phase of reflected RF energy and some derived
data, which is derived from amplitude and/or phase data (e.g., from
the "raw data"), can provide improved correspondence to a health
parameter, such as blood glucose level. Thus, in an embodiment,
some data is derived from the amplitude and/or phase data that is
generated by the sensor system in response to the received RF
energy and the derived data is used, often in conjunction with the
amplitude and/or phase data, to determine a value that corresponds
to a health parameter (e.g., the blood glucose level) of a person.
For example, the data is derived from the raw data records that
include the data depicted in FIGS. 23, 24, and 26. For example, raw
data records are accumulated over time and statistical data is
derived from the accumulated raw data records. The statistical
data, typically along with at least some portion of the raw data,
is then used to determine a value of a health parameter of a
person.
[0154] Although it has been found that derived data from the
amplitude and/or phase data can provide improved correspondence to
a health parameter, such as blood glucose level, the particular
model that provides a desired level of correspondence (e.g., that
meets a predetermined accuracy) may need to be learned in response
to a specific set of operating conditions. Thus, in an embodiment,
a learning process (e.g., machine learning) is implemented to
identify and train a model that provides an acceptable
correspondence to a health parameter such as blood glucose
level.
[0155] FIG. 27 illustrates a system 2700 and process for machine
learning that can be used to identify and train a model that
reflects correlations between raw data, derived data, and control
data. For example, the machine learning process may be used to
identify certain statistics (e.g., standard deviation of amplitude
and/or phase over time) that can be used to improve the
correspondence of determined values to actual health parameters
(such as blood glucose levels) in a person. The machine learning
process can also be used to train a model with training data so
that the trained model can accurately and reliably determine values
for health parameters such as blood glucose level, blood pressure,
and/or heart rate in monitoring devices that are deployed in the
field. With reference to FIG. 27, the system 2700 includes a sensor
system 2710, a machine learning engine 2760, a trained model
database 2762, and a control element 2764.
[0156] In an embodiment, the sensor system 2710 is similar to or
the same as the sensor system described above. For example, the
sensor system is configured to implement stepped frequency scanning
in the 2-6 GHz and/or 122-126 GHz frequency range using two
transmit antennas and four receive antennas. The sensor system
generates and outputs raw data to the machine learning engine that
can be accumulated and used as described below.
[0157] In an embodiment, the control element 2764 is configured to
provide a control sample to the sensor system 2710. For example,
the control element includes a sample material 2766 (e.g., a fluid)
that has a known blood glucose level that is subjected to the
sensor system. Additionally, in an embodiment, the control element
is configured to provide control data to the machine learning
engine that corresponds to the sample material. For example, the
control element may include a sample material that has a known
blood glucose level that changes as a function of time and the
change in blood glucose level as a function of time (e.g., Z(t)
mg/dL) is provided to the machine learning engine 2760 in a manner
in which the raw data from the sensor system 2710 and the control
data can be time matched (e.g., synchronized). In another
embodiment, the control element 2764 includes a sample material
that includes a static parameter, e.g., a static blood glucose
level in mg/dL, and the static parameter is manually provided to
the machine learning engine 2760 as the control data. For example,
a particular sample is provided within range of RF energy 2770 that
is transmitted from the sensor system (e.g., within a few
millimeters), the concentration of the sample is provided to the
machine learning engine (e.g., manually entered), and the sensor
system accumulates digital data that corresponds to the received RF
energy (including a reflected portion of the transmitted RF energy)
and that is correlated to the sample. In one embodiment, the sample
material is provided in a container such as a vial and in another
embodiment, the control element includes a person that is
simultaneously being monitored by the sensor system (e.g., for the
purposes of machine learning) and by a second, trusted, control
monitoring system. For example, the control element includes a
person who's blood glucose level, blood pressure, and/or heart rate
is being monitored by a known (e.g., clinically accepted) blood
glucose level, blood pressure, and/or heart rate monitor while the
person is simultaneously being monitored by the sensor system. The
blood glucose level, blood pressure, and/or heart rate information
from the known blood glucose level, blood pressure, and/or heart
rate monitor is provided to the machine learning engine as control
data.
[0158] In an embodiment, the machine learning engine 2760 is
configured to process the raw data received from the sensor system
2710, e.g., as raw data records, and the control data received from
the control element 2764 to learn a correlation, or correlations,
that provides acceptable correspondence to a health parameter such
as blood glucose levels. For example, the machine learning engine
is configured to receive raw data from the sensor system, to derive
data from the raw data such as statistical data, and to compare the
derived data (and likely at least some portion of the corresponding
raw data) to the control data to learn a correlation, or
correlations, that provides acceptable correspondence between a
determined value of a health parameter and a controlled, or known
value, of the health parameter. In an embodiment, the machine
learning engine is configured to derive statistics from the raw
data such as a standard deviation, a moving average, and a moving
mean. For example, the machine learning engine may derive the
standard deviation of the amplitude and/or phase of the received RF
energy and then correlate the derived statistic(s) and the raw data
to the control data to find a correlation that provides an
acceptable correspondence between the raw data, the derived data,
and the actual value of the health parameter as provided in the
control data. In an embodiment, correspondence between the raw
data, the derived data, and the actual values of the health
parameter in a control sample is expressed in terms of a
correspondence threshold, which is indicative of, for example, the
correspondence between values of a health parameter generated in
response to the raw data, the derived data, and actual values of
the health parameter in a control sample. For example, a
correspondence is expressed as a percentage of correspondence to
the actual value of the control sample such that a generated
concentration value of a blood glucose level of 135 mg/dL and a
value of a control sample at 140 mg/dL has a correspondence of
135/140=96.4%. In an embodiment, a correspondence threshold can be
set to accept only those correlations that produce correspondence
that meets a desired correspondence threshold. In an embodiment, a
correspondence threshold of a generated value to the value of a
control sample of within .+-.10% of the control sample is
acceptable correspondence. In another embodiment, a correspondence
threshold of within .+-.10% of the control sample in 95% of the
measurements is acceptable correspondence.
[0159] FIG. 28 is an example of a process flow diagram of a method
for implementing machine learning using, for example, the system
described above with reference to FIG. 27 to select a correlation
(e.g., a model or algorithm) that provides acceptable
correspondence between values of a health parameter generated in
response to the raw data, the derived data, and actual values of
the health parameter in the control samples. At block 2802, raw
data is obtained from the sensor system. At block 2804, the raw
data is correlated to known control data, such as known blood
glucose levels. At decision point 2806, it is determined whether a
correlation between the raw data and the control data is
acceptable, e.g., whether the correspondence is within an
acceptable threshold. If it is determined that there is an
acceptable correspondence, then the process proceeds to block 2808,
where the correlation (e.g., a model or algorithm) is saved and
then the initial learning process is ended. If at decision point
2806 it is determined that there is not an acceptable
correspondence between the raw data and the control data (e.g., the
correspondence is not within an acceptable threshold), then the
process proceeds to block 2810. At block 2810, additional data is
derived from the raw data. For example, the machine learning engine
may derive a statistic or statistics from the raw data such as a
standard deviation, a moving average, and a moving mean. For
example, the machine learning engine may derive the standard
deviation of the amplitude and/or phase of the received RF energy.
At decision point 2812, it is determined whether a correlation
between the raw data, the derived data, and the control data is
acceptable (e.g., the correspondence is within an acceptable
threshold). If it is determined that there is an acceptable
correspondence between the raw data, the derived data, and the
control data, then the process proceeds to block 2814, where the
correlation (e.g., a model or algorithm) is saved and then the
initial learning process is ended. If at decision point 2812 it is
determined that there is not an acceptable correspondence between
the raw data, the derived data, and the control data (e.g., the
correspondence is not within an acceptable threshold), then the
process returns to block 2810. At block 2810, additional data is
derived from the raw data and/or from the derived data.
[0160] For example, a different statistic, or statistics, is
derived from the raw data and/or from the previously derived data.
In an embodiment, the exploration of correlations between the raw
data, the derived data, and the control data is an iterative
process that converges on a correlation, or correlations, which
provides acceptable correspondence between the raw data, the
derived data, and the control data. In an embodiment, the machine
learning process can be repeatedly used to continue to search for
correlations that may improve the correspondence between the raw
data, the derived data, and the control data to improve the
accuracy of health parameter monitoring.
[0161] In an embodiment, the above-described process is used for
algorithm selection and/or model building as is done in the field
of machine learning. In an embodiment, algorithm selection and/or
model building involves supervised learning to recognize patterns
in the data (e.g., the raw data, the derived data, and/or the
control data). In an embodiment, the algorithm selection process
may involve utilizing regularized regression algorithms (e.g.,
Lasso Regression, Ridge Regression, Elastic-Net), decision tree
algorithms, and/or tree ensembles (random forests, boosted
trees).
[0162] In an embodiment, acceptable correlations that are learned
by the machine learning engine are trained by the machine learning
engine to produce a trained model, or trained models, that can be
deployed in the field to monitor a health parameter of a person.
Referring back to FIG. 27, a model that is trained by the machine
learning engine 2760 is held in the trained model database 2762. In
an embodiment, the trained model database may store multiple models
that have been found to provide acceptable correspondence between
generated values of a health parameter and the actual values of the
health parameter as provided in the control data. Additionally, the
trained model database 2762 may provide rules on how to apply the
model in deployed sensor systems. For example, different models may
apply to different deployment conditions, e.g., depending on the
location of the RF front-end relative to a blood vessel,
environmental conditions, etc.
[0163] In an embodiment, operation of the system 2700 shown in FIG.
27 to generate training data and to train a model using the
training data involves providing a control sample in the control
element 2764 and then operating the sensor system 2700 to implement
stepped frequency scanning over a desired frequency range that is
within, for example, the 2-6 GHz and/or 122-126 GHz frequency
range. For example, control data corresponding to the control
sample 2766 is provided to the machine learning engine 2760 and raw
data generated from the sensor system 2710 is provided to the
machine learning engine. The machine learning engine generates
training data by combining the control data with the stepped
frequency scanning data in a time synchronous manner The machine
learning engine processes the training data to train a model, or
models, which provides an acceptable correspondence between
generated values of a health parameter and the control data. The
model, or models, is stored in the trained model database 2762,
which can then be applied to a system 2700 that is deployed in the
field to monitor a health parameter of a person. In an embodiment,
the sensor system is exposed to multiple different samples under
multiple different operating conditions to generate a rich set of
training data.
[0164] In an embodiment, the goal of the training process is to
produce a trained model that provides a high level of accuracy and
reliability in monitoring a health parameter in a person over a
wide set of parameter ranges and operational and/or environmental
conditions. For example, the correspondence of a model during
training can be expressed in terms of a correspondence threshold,
which is indicative of, for example, the correspondence between
values of a health parameter generated in response to the raw data,
the derived data, and actual values of the health parameter in a
control sample. For example, a correspondence is expressed as a
percentage of correspondence to the actual value of the control
sample such that a generated concentration value of a blood glucose
level of 135 mg/dL and a value of a control sample at 140 mg/dL has
a correspondence of 135/140=96.4%. In an embodiment, a
correspondence threshold can be set for a trained model so that the
trained model produces correspondence that meets a desired
correspondence threshold. In an embodiment, a correspondence
threshold of a generated value to the value of a control sample of
within .+-.10% of the control sample is acceptable correspondence
for a trained model. In another embodiment, a correspondence
threshold of within .+-.10% of the control sample in 95% of the
measurements is acceptable correspondence for a trained model.
[0165] In an embodiment, the correspondence between the raw and/or
derived data and the control data may change in response to
different factors including, for example, over different blood
glucose levels, different monitoring locations, different
environmental conditions, etc. Thus, in some embodiments, the
trained model database 2762 may include multiple different trained
models that are applicable to certain conditions. Additionally, the
trained model database may evolve over time as more information is
gathered and/or as different correlations are discovered.
[0166] As described above, the model training process utilizes raw
data (e.g., in the form of raw data records) as inputs into the
machine learning engine. FIG. 29 is an example of a table of a raw
data record (e.g., digital data) generated during stepped frequency
scanning that is used to generate the training data. The raw data
record includes time t1, a known blood glucose level (e.g., a
control sample with a known concentration of glucose in mg/dL, Z
mg/dL) at the time t1, TX/RX frequency at the time t1, RX1
amplitude/phase, RX2 amplitude/phase, RX3 amplitude/phase, and RX4
amplitude/phase at the time t1. In the example of FIG. 29, the raw
data record includes the glucose level of the control sample at the
same time the amplitude and phase of the RF energy was received by
the sensor system, thus, the control data is combined with the
stepped frequency scanning data in a time synchronous manner In
addition, the raw data records that are used to generate the
training data may include some or all of the parameters identified
in FIGS. 23 and 24. For example, the raw data records and the
corresponding training data may include other variable and/or fixed
parameters that correspond to the stepped frequency scanning
operation to provide a rich set of parameters from which to
generate the training data.
[0167] In a stepped frequency scanning operation, multiple raw data
records are generated as the sensor system scans across a frequency
range. FIGS. 30A-30D are tables of at least portions of raw data
records that are generated during a learning process that spans the
time of t1-tn, where n corresponds to the number (e.g., an integer
of 2 or greater) of time intervals, T, in the stepped frequency
scanning Each of the raw data records includes control data (e.g.,
known glucose level, Z mg/dL) that is combined with stepped
frequency scanning data in a time synchronous manner
[0168] With reference to FIG. 30A, at time, t1, the raw data record
includes the time, t1, a known blood glucose level (e.g., Z1 in
mg/dL) at time t1, a TX/RX frequency (e.g., X GHz) at time t1, RX1
amplitude/phase at time t1 (ampl1-t1/ph1-t1), RX2 amplitude/phase
at time t1 (ampl2-t1/ph2-t1), RX3 amplitude/phase at time t1
(ampl3-t1/ph3-t1), and RX4 amplitude/phase at time t1
(ampl4-t1/ph4-t1). In the stepped frequency scanning, at the next
time, t2, the frequency is changed by one step size, e.g.,
incremented by .DELTA.f. In an embodiment, the stepped frequency
scanning operation generates 200 raw data records per second, e.g.,
a sample rate of 200 samples/second. With reference to FIG. 30B, at
time, t2, the raw data record includes the time, t2, a known blood
glucose level (e.g., Z2 in mg/dL) at time t2, a TX/RX frequency
(e.g., X+.DELTA.f GHz) at time t2, RX1 amplitude/phase at time t2
(ampl1-t2/ph1-t2), RX2 amplitude/phase at time t2
(ampl2-t2/ph2-t2), RX3 amplitude/phase at time t2
(ampl3-t2/ph3-t2), and RX4 amplitude/phase at time t2
(ampl4-t2/ph4-t2). With reference to FIG. 30C, at time, t3, the raw
data record includes the time, t3, a known blood glucose level
(e.g., Z3 in mg/dL) at time t3, a TX/RX frequency (e.g.,
X+2.DELTA.f GHz) at time t3, RX1 amplitude/phase at time t3
(ampl1-t3/ph1-t3), RX2 amplitude/phase at time t3
(ampl2-t3/ph2-t3), RX3 amplitude/phase at time t3
(ampl3-t3/ph3-t3), and RX4 amplitude/phase at time t3
(ampl4-t3/ph4-t3). With reference to FIG. 30D, at time, tn, the raw
data record includes the time, tn, a known blood glucose level
(e.g., Zn in mg/dL) at time tn, a TX/RX frequency (e.g.,
X+(n-1).DELTA.f GHz) at time tn, RX1 amplitude/phase at time tn
(ampl1-tn/ph1-tn), RX2 amplitude/phase at time tn
(ampl2-tn/ph2-tn), RX3 amplitude/phase at time tn
(ampl3-tn/ph3-tn), and RX4 amplitude/phase (ampl4-tn/ph4-tn) at
time tn.
[0169] As illustrated above, raw data is collected on a per-antenna
basis for the amplitude and/or phase of the received RF energy. Raw
data collected on a per-antenna basis for amplitude and phase for
the example of FIGS. 30A-30D may include: [0170] ampl1: ampl1-t1,
ampl1-t2, ampl1-t3, . . . , ampl1-tn; [0171] ampl2: ampl2-t1,
ampl2-t2, ampl2-t3, . . . , ampl2-tn; [0172] ampl3: ampl3-t1,
ampl3-t2, ampl3-t3, . . . , ampl3-tn; [0173] ampl4; ampl4-t1,
ampl4-t2, ampl4-t3, . . . , ampl4-tn; [0174] ph1: ph1-t1, ph1-t2,
ph1-t3, . . . , ph1-tn; [0175] ph2: ph2-t1, ph2-t2, ph2-t3, . . . ,
ph2-tn; [0176] ph3: ph3-t1, ph3-t2, ph3-t3, . . . , ph3-tn); and
[0177] ph4: ph4-t1, ph4-t2, ph4-t3, . . . , ph4-tn).
[0178] In the example of FIGS. 30A-30D, the standard deviation may
be calculated on a per-antenna basis for the amplitude and phase
and is a function of the following raw data elements: [0179]
.sigma.(ampl1)=f(ampl1-t1+ampl1-t2+ampl1-t3+ . . . +ampl1-tn);
[0180] .sigma.(ampl2)=f(ampl2-t1+ampl2-t2+ampl2-t3+ . . .
+ampl2-tn); [0181] .sigma.(ampl3)=f(ampl3-t1+ampl3-t2+ampl3-t3+ . .
. +ampl3-tn); [0182] .sigma.(ampl4)=f(ampl4-t1+ampl4-t2+ampl4-t3+ .
. . +ampl4-tn); [0183] .sigma.(ph1)=f(ph1-t1+ph1-t2+ph1-t3+ . . .
+ph1-tn); [0184] .sigma.(ph2)=f(ph2-t1+ph2-t2+ph2-t3+ . . .
+ph2-tn); [0185] .sigma.(ph3)=f(ph3-t1+ph3-t2+ph3-t3+ . . .
+ph3-tn); and [0186] .sigma.(ph4)=f(ph4-t1+ph4-t2+ph4-t3+ . . .
+ph4-tn).
[0187] In an embodiment, data is derived on a per-antenna basis. In
other embodiments, data such as statistics can be derived from data
corresponding to different combinations of antennas.
[0188] Raw data records collected over time can be used as
described above to learn correlations (e.g., a model or algorithm)
between the raw data, derived data, and the control data and to
train a model. In an embodiment, a rich set of training data is
collected and processed to train a model that can provide accurate
and reliable measurements of a health parameter such as blood
glucose level, blood pressure, and/or heart rate. In an embodiment,
the raw data including amplitude and phase and the derived data
including the standard deviation of the amplitude has been found to
correspond well to the health parameter of blood glucose level.
[0189] Once correlations between the raw data, the derived data,
and the control data have been learned and a model has been
trained, a sensor system can be deployed into the field for use in
monitoring a health parameter of a person, such as the blood
glucose level. FIG. 31 illustrates a system 3100 for health
parameter monitoring that utilizes a sensor system similar to or
the same as the sensor system described above. With reference to
FIG. 31, the system includes a sensor system 3110, a health
parameter determination engine 3180, and a trained model database
3182.
[0190] In an embodiment, the sensor system 3110 is similar to or
the same as the sensor system described above. For example, the
sensor system is configured to implement stepped frequency scanning
in the 2-6 GHz and/or 122-126 GHz frequency range using two
transmit antennas and four receive antennas. The sensor system
generates and outputs raw data to the health parameter
determination engine 3180 that can be accumulated and used to
generate and output a value that corresponds to a health
parameter.
[0191] A model (or models) that is trained by the machine learning
engine as described above is held in the trained model database
3182. In an embodiment, the trained model database may store
multiple models that have been trained to provide acceptable
correspondence between a generated value of a health parameter and
the actual value of the health parameter as provided in the control
data. Additionally, the trained model database may provide rules on
how to apply trained models in deployed sensor systems. In an
embodiment, the trained model database includes memory for storing
a trained model, or models. The memory may include, for example,
RAM, SRAM, and/or SSD.
[0192] In an embodiment, the health parameter determination engine
3180 is configured to generate an output that corresponds to a
health parameter in response to the raw data received from the
sensor system 3110, derived data, and using a trained model that is
stored in the trained model database 3182. For example, the health
parameter determination engine 3180 outputs a value that indicates
a blood glucose level in mg/dL or some other indication of the
blood glucose level. In other embodiments, the health parameter
determination engine may output a value that is an indication of a
person's heart rate (e.g., in beats per minute) and/or an
indication of a person's blood pressure (e.g., in millimeters of
mercury, mmHg). In other embodiments, the "values" output by the
health parameter determination engine may correspond to a health
parameter in other ways. For example, the output value may indicate
a value such as "high," "medium," "low" with respect to a health
parameter (e.g., a high blood glucose level, a medium blood glucose
level, or a low blood glucose level relative to a blood glucose
scale), the output value may indicate a color, such as green,
yellow, or red that indicates a health parameter, or the output
value, may indicate a range of values, such as 130-140 mg/dL blood
glucose, 70-80 beats per minute, or 110-120 mmHg blood pressure. In
an embodiment, the health parameter determination engine recognizes
patterns in the raw and/or derived data and applies the recognized
patterns to the trained model to generate an output that
corresponds to a health parameter in a person. The health parameter
determination engine may be implemented by a digital processor,
such as a CPU or MCU, in conjunction with computer readable
instructions that executed by the digital processor.
[0193] In an embodiment, operation of the system 3100 shown in FIG.
31 involves bringing a portion of a person's anatomy 3186 (such as
a wrist, arm, or ear area) into close proximity to the sensor
system 3110 (or bringing the sensor system into close proximity to
the portion of a person's anatomy) and operating the sensor system
to implement stepped frequency scanning over a frequency range,
e.g., in the range of 122-126 GHz such that transmitted RF energy
3170 penetrates below the surface of the person's skin. Raw data
generated from implementing the stepped frequency scanning is
output from the sensor system and received at the health parameter
determination engine 3180. The health parameter determination
engine processes the raw data in conjunction with at least one
trained model from the trained model database 3182 to generate a
value that corresponds to a health parameter of the person, e.g., a
value that corresponds to the blood glucose level of the person. In
an embodiment, the value that corresponds to the health parameter
is output, for example, as a graphical indication of the blood
glucose level. In an embodiment, the generated value may be stored
in a health parameter database for subsequent access.
[0194] In an embodiment, the system 3100 depicted in FIG. 31 is
implemented in a device such as a smartwatch or smartphone. In
other embodiments, some portion of the system (e.g., the RF
front-end) is implemented in a device, such as a dongle, a patch, a
smartphone case, or some other device and the health parameter
determination engine and the trained model correlations database is
implemented in a nearby device such as a smartphone. For example,
in one embodiment, the sensor system is embodied in a device that
attaches near the ear of a person and raw data is communicated via
a wireless connection to a device such as a smartphone that
processes the raw data to generate a value that corresponds to the
blood glucose level of the person.
[0195] FIG. 32 is a process flow diagram of a method for monitoring
a health parameter in a person. At block 3202, radio waves are
transmitted below the skin surface of a person and across a range
of stepped frequencies. At block 3204, radio waves are received on
a two-dimensional array of receive antennas, the received radio
waves including a reflected portion of the transmitted radio waves
across the range of stepped frequencies. At block 3206, data that
corresponds to the received radio waves is generated, wherein the
data includes amplitude and phase data. At block 3208, a value that
is indicative of a health parameter in the person is determined in
response to the amplitude and phase data.
[0196] FIG. 33 is a process flow diagram of another method for
monitoring a health parameter in a person. At block 3302, radio
waves are transmitted below the skin surface of a person and across
a range of stepped frequencies. At block 3304, radio waves are
received on a two-dimensional array of receive antennas, the
received radio waves including a reflected portion of the
transmitted radio waves across the range of stepped frequencies. At
block 3306, data that corresponds to the received radio waves is
generated, wherein the data includes amplitude and phase data. At
block 3308, data is derived from at least one of the amplitude and
phase data. At block 3310, a value that is indicative of a health
parameter in the person is determined in response to the derived
data.
[0197] FIG. 34 is a process flow diagram of a method for training a
model for use in monitoring a health parameter in a person. At
block 3402, control data that corresponds to a control element is
received, wherein the control data corresponds to a health
parameter of a person. At block 3404, stepped frequency scanning
data that corresponds to radio waves that have reflected from the
control element is received, wherein the stepped frequency scanning
data includes frequency and corresponding amplitude and phase data
over a range of frequencies. At block 3406, training data is
generated by combining the control data with the stepped frequency
scanning data in a time synchronous manner At block 3408, a model
is trained using the training data to produce a trained model,
wherein the trained model correlates stepped frequency scanning
data to values that are indicative of a health parameter of a
person.
[0198] Although the operations of the method(s) herein are shown
and described in a particular order, the order of the operations of
each method may be altered so that certain operations may be
performed in an inverse order or so that certain operations may be
performed, at least in part, concurrently with other operations. In
another embodiment, instructions or sub-operations of distinct
operations may be implemented in an intermittent and/or alternating
manner
[0199] It should also be noted that at least some of the operations
for the methods described herein may be implemented using software
instructions stored on a computer useable storage medium for
execution by a computer. As an example, an embodiment of a computer
program product includes a computer useable storage medium to store
a computer readable program.
[0200] The computer-useable or computer-readable storage medium can
be an electronic, magnetic, optical, electromagnetic, infrared, or
semiconductor system (or apparatus or device). Examples of
non-transitory computer-useable and computer-readable storage media
include a semiconductor or solid state memory, magnetic tape, a
removable computer diskette, a random access memory (RAM), a
read-only memory (ROM), a rigid magnetic disk, and an optical disk.
Current examples of optical disks include a compact disk with read
only memory (CD-ROM), a compact disk with read/write (CD-R/W), and
a digital video disk (DVD).
[0201] Alternatively, embodiments of the invention may be
implemented entirely in hardware or in an implementation containing
both hardware and software elements. In embodiments which use
software, the software may include but is not limited to firmware,
resident software, microcode, etc.
[0202] Although specific embodiments of the invention have been
described and illustrated, the invention is not to be limited to
the specific forms or arrangements of parts so described and
illustrated. The scope of the invention is to be defined by the
claims appended hereto and their equivalents.
* * * * *